Real-world images captured in remote sensing, image or video retrieval, and outdoor surveillance are often degraded due to poor weather conditions, such as rain and mist. These conditions introduce artifacts that make visual analysis challenging and limit the performance of high-level computer vision methods. In time-critical applications, it is vital to develop algorithms that automatically remove rain without compromising the quality of the image contents. This article proposes a novel approach called QSAM-Net, a quaternion multi-stage multiscale neural network with a self-attention module. The algorithm requires significantly fewer parameters by a factor of 3.98 than the real-valued counterpart and state-of-the-art methods while improving the visual quality of the images. The extensive evaluation and benchmarking on synthetic and real-world rainy images demonstrate the effectiveness of QSAM-Net. This feature makes the network suitable for edge devices and applications requiring near real-time performance. Furthermore, the experiments show that the improved visual quality of images also leads to better object detection accuracy and training speed.

Single-image haze removal is challenging due to its ill-posed nature. The breadth of real-world scenarios makes it difficult to find an optimal dehazing approach that works well for various applications. This article addresses this challenge by utilizing a novel robust quaternion neural network architecture for single-image dehazing applications. The architecture's performance to dehaze images and its impact on real applications, such as object detection, is presented. The proposed single-image dehazing network is based on an encoder-decoder architecture capable of taking advantage of quaternion image representation without interrupting the quaternion dataflow end-to-end. We achieve this by introducing a novel quaternion pixel-wise loss function and quaternion instance normalization layer. The performance of the proposed QCNN-H quaternion framework is evaluated on two synthetic datasets, two real-world datasets, and one real-world task-oriented benchmark. Extensive experiments confirm that the QCNN-H outperforms state-of-the-art haze removal procedures in visual quality and quantitative metrics. Furthermore, the evaluation shows increased accuracy and recall of state-of-the-art object detection in hazy scenes using the presented QCNN-H method. This is the first time the quaternion convolutional network has been applied to the haze removal task.

Image restoration techniques process degraded images to highlight obscure details or enhance the scene with good contrast and vivid color for the best possible visibility. Poor illumination condition causes issues, such as high-level noise, unlikely color or texture distortions, nonuniform exposure, halo artifacts, and lack of sharpness in the images. This article presents a novel end-to-end trainable deep convolutional neural network called the deep perceptual image enhancement network (DPIENet) to address these challenges. The novel contributions of the proposed work are: 1) a framework to synthesize multiple exposures from a single image and utilizing the exposure variation to restore the image and 2) a loss function based on the approximation of the logarithmic response of the human eye. Extensive computer simulations on the benchmark MIT-Adobe FiveK and user studies performed using Google high dynamic range, DIV2K, and low light image datasets show that DPIENet has clear advantages over state-of-the-art techniques. It has the potential to be useful for many everyday applications such as modernizing traditional camera technologies that currently capture images/videos with under/overexposed regions due to their sensors limitations, to be used in consumer photography to help the users capture appealing images, or for a variety of intelligent systems, including automated driving and video surveillance applications.

The application of Artificial Intelligence in dental healthcare has a very promising role due to the abundance of imagery and non-imagery-based clinical data. Expert analysis of dental radiographs can provide crucial information for clinical diagnosis and treatment. In recent years, Convolutional Neural Networks have achieved the highest accuracy in various benchmarks, including analyzing dental X-ray images to improve clinical care quality. The Tufts Dental Database, a new X-ray panoramic radiography image dataset, has been presented in this paper. This dataset consists of 1000 panoramic dental radiography images with expert labeling of abnormalities and teeth. The classification of radiography images was performed based on five different levels: anatomical location, peripheral characteristics, radiodensity, effects on the surrounding structure, and the abnormality category. This first-of-its-kind multimodal dataset also includes the radiologist's expertise captured in the form of eye-tracking and think-aloud protocol. The contributions of this work are 1) publicly available dataset that can help researchers to incorporate human expertise into AI and achieve more robust and accurate abnormality detection; 2) a benchmark performance analysis for various state-of-the-art systems for dental radiograph image enhancement and image segmentation using deep learning; 3) an in-depth review of various panoramic dental image datasets, along with segmentation and detection systems. The release of this dataset aims to propel the development of AI-powered automated abnormality detection and classification in dental panoramic radiographs, enhance tooth segmentation algorithms, and the ability to distill the radiologist's expertise into AI.

Predicting future sensory states is crucial for learning agents such as robots, drones, and autonomous vehicles. In this paper, we couple multiple sensory modalities with exploratory actions and propose a predictive neural network architecture to address this problem. Most existing approaches rely on large, manually annotated datasets, or only use visual data as a single modality. In contrast, the unsupervised method presented here uses multi-modal perceptions for predicting future visual frames. As a result, the proposed model is more comprehensive and can better capture the spatio-temporal dynamics of the environment, leading to more accurate visual frame prediction. The other novelty of our framework is the use of sub-networks dedicated to anticipating future haptic, audio, and tactile signals. The framework was tested and validated with a dataset containing 4 sensory modalities (vision, haptic, audio, and tactile) on a humanoid robot performing 9 behaviors multiple times on a large set of objects. While the visual information is the dominant modality, utilizing the additional non-visual modalities improves the accuracy of predictions.

The coronavirus (COVID-19) pandemic has been adversely affecting people's health globally. To diminish the effect of this widespread pandemic, it is essential to detect COVID-19 cases as quickly as possible. Chest radiographs are less expensive and are a widely available imaging modality for detecting chest pathology compared with CT images. They play a vital role in early prediction and developing treatment plans for suspected or confirmed COVID-19 chest infection patients. In this paper, a novel shape-dependent Fibonacci-p patterns-based feature descriptor using a machine learning approach is proposed. Computer simulations show that the presented system (1) increases the effectiveness of differentiating COVID-19, viral pneumonia, and normal conditions, (2) is effective on small datasets, and (3) has faster inference time compared to deep learning methods with comparable performance. Computer simulations are performed on two publicly available datasets; (a) the Kaggle dataset, and (b) the COVIDGR dataset. To assess the performance of the presented system, various evaluation parameters, such as accuracy, recall, specificity, precision, and f1-score are used. Nearly 100% differentiation between normal and COVID-19 radiographs is observed for the three-class classification scheme using the lung area-specific Kaggle radiographs. While Recall of 72.65 ± 6.83 and specificity of 77.72 ± 8.06 is observed for the COVIDGR dataset.

Emulating human behaviours in automated image quality assessment (IQA) enables a comparator framework to remove the differences in human bias naturally. Based on the observation of the practical applications of IQA, this study focuses on similar‐content image quality comparison based on a new image quality comparator (IQC). Outstanding proven IQAs can be utilised in this comparator to achieve a new non‐linear combination strategy to boost the IQAs' performance in image quality comparison. For both input images to be compared, proven IQAs are utilised to obtain nine features from each image, yielding 18 total features. Then, a four‐layer comparison network conducts a classification task to indicate which input image has better quality. In the training phase, the commonly used human scores as training labels are replaced with pairwise comparison results that are automatically generated from assigned distortion level differences. By not utilising human score in training phase, this IQC shows two advantages: (i) it removes huge labor and time cost to collect the human scores and (ii) it solves the problem of over‐fitting benefiting from simplicity of creating a large image training dataset. Furthermore, the experimental tests and cross‐dataset validation comparison tests demonstrate its impressive performance.

Cross-modality face recognition is an emerging topic due to the wide-spread usage of different sensors in day-to-day life applications. The development of face recognition systems relies greatly on existing databases for evaluation and obtaining training examples for data-hungry machine learning algorithms. However, currently, there is no publicly available face database that includes more than two modalities for the same subject. In this work, we introduce the Tufts Face Database that includes images acquired in various modalities: photograph images, thermal images, near infrared images, a recorded video, a computerized facial sketch, and 3D images of each volunteer's face. An Institutional Research Board protocol was obtained and images were collected from students, staff, faculty, and their family members at Tufts University. The database includes over 10,000 images from 113 individuals from more than 15 different countries, various gender identities, ages, and ethnic backgrounds. The contributions of this work are: 1) Detailed description of the content and acquisition procedure for images in the Tufts Face Database; 2) The Tufts Face Database is publicly available to researchers worldwide, which will allow assessment and creation of more robust, consistent, and adaptable recognition algorithms; 3) A comprehensive, up-to-date review on face recognition systems and face datasets.

This paper introduces a new concept, selective color transfer (SCT), to allow for color experimentation and visualization on a selected object within an image, without affecting any other image content or color. To implement this new concept, this paper proposes a new fast SCT-I algorithm for image input data and a new SCT-V algorithm for video input data. For the SCT-I algorithm, its fundamental novelties include: 1) utilizing a new hue-division-based color region segmentation (HCR) based on defining a new nonlinear and circular property of the hue spectrum; 2) developing two new color artifact suppression strategies, including color region integration and circular statistical calculation; and 3) applying a new truncated color transfer equation to solve the problem of mismatches in the data range. The SCT-V algorithm utilizes: 1) object tracking to locate the target of interest (TOI) within each video frame; 2) the SCT-I algorithm to update the original color within TOIs; and 3) an expected color interpolation process to support different special color animation effects. The experimental results demonstrate: 1) there are no residual color artifacts introduced; 2) the approaches have tremendous flexibility for modifying an object's color; and 3) these algorithms have a low computational cost. Furthermore, these algorithms require minimal manual intervention for the easy use of ordinary users. The performance analysis demonstrates the potential of these two methods for various applications, including creating special effects in videos, making histology images much clearer for medical inspection, and enabling low-cost experimentation of manufacturing the design of a product.

The advancement of wearable eye-tracking technology enables cognitive researchers to capture vast amounts of eye gaze information while participants are completing specific tasks without restrictions on their movement. However, while eye trackers can overlay a gaze indicator on the scene video, identifying the specific objects being looked at and analyzing the resulting dataset are accomplished mostly by manual annotation. This method is a cost-prohibitive and time-consuming approach that is prone to human error. Such analytic difficulty limits researchers' ability to data mine the information efficiently, ultimately restricting the number of scenarios that can feasibly be conducted within budget. Here, the first fully automated solution for eye-tracking data analysis is presented, which eliminates the need for manual annotation. The proposed software architecture, gaze to object classification (GoC), processes the gaze-overlaid video from commercially available wearable eye trackers, recognizes and classifies the specific object a user is focusing on and calculates the gaze duration time. GoC utilizes an image cross-correlation method to locate the gaze indicator and an image similarity measurement to support faster processing. The presented system has been successfully adopted by cognitive psychologists. GoC's exceptional performance in analyzing a case study spanning over 50 h of mobile eye-tracking is presented. The accuracy and a cost-analysis comparison between GoC and state-of-the-art manual annotation software are provided. GoC has game-changing potential for increasing the ecological validity of using eye-tracking technology in cognitive research.

Fine Art Photography is one of the most popular art forms, which creates lasting impressions that elicit various human emotional reactions. Photo aesthetic enhancement aims at improving the aesthetic level of the photo to please humans by updating color appearance or modifying the geometry structure of objects within that photo. Even though several aesthetic enhancement methods have been proposed, to our knowledge, there is no research to explore, highlight, and accentuate photos’ intrinsic aesthetic value to elicit a stronger response from the human observer about the photos’ theme. To meet this challenge, a new multimedia technology called automatic color theme--based aesthetic enhancement (CT-AEA) is proposed by leveraging big online data to perform timely collection and learning of humans’ current aesthetic perception-behavior over photos and color themes in art, fashion, and design. Unlike existing aesthetic enhancement that examines the composition, such as the geometric structure of the image contents and color/luminance-related (color tone and luminance distribution) characteristics, this CT-AEA takes into consideration the importance of a suitable color theme, namely a set of dominant colors for the design when assessing the aesthetic appearance of a photo. This algorithm is composed of (1) utilizing the knowledge gained from the human evaluator's perception of beauty from existing online datasets, rather than simply applying prior existing knowledge of color harmony theory; (2) developing a new color theme difference equation that exhibits order-invariance and percentage-sensitive properties; (3) designing an optimal color theme recommendation to maximize the aesthetic performance, while minimizing the color modification cost to solve the problems of color inconsistencies and distortion. Experimental results, quantitative measure, and comparison tests demonstrate the algorithm's effectiveness, advantages, and potential for use in many color-related art and design applications.

Medical imaging systems often require image enhancement to visualize images of the human body and its organs. This would help medical professionals in irregularity or abnormality detection and diagnosis. This paper demonstrates a method to enhance medical related images. The proposed algorithm uses techniques, such as, guided filtering, edge enhancement, contrast stretching, and image fusion to enhance low resolution images. Visually, the proposed method produces better or comparable enhanced images than several state-of-the-art methods. In addition, we also test the performance of the proposed method with the method mentioned in [1].

Edge detection is an important task in image processing, and the quality of further processing is often reflected by the quality of edge detector outputs. Therefore, it is necessary to develop effective edge map quality measures to assist in evaluating the performance of edge detectors. Objective evaluation measures are crucial in automatically determining the optimal edge map for a given image or an application, as well as its parameter values. In this paper, a new reference-based edge measure (RBEM) is introduced to evaluate the performance of edge detector outputs relative to a ground truth. The new measure fuses four component metrics, based on edge pixel presence, edge corner localization, thick edge occurrence, and edge connectivity. Each of these metrics can be used separately or as a standalone measure to evaluate the quality of an edge map in terms of specific characteristics. The effectiveness of the proposed measure is demonstrated for selecting the best edge detector among several edge detectors, as well as for selecting the optimal parameter values, for both synthetic images and natural images. Experimental results show that the presented RBEM outperforms the existing methods according to subjective evaluation mean opinion scores, as it considers more important visual features in its evaluation.

A S students starting out in our academic careers, we are usually very binary in our perspectivesVwe either like something or hate it.For example, students may say they love programming but dread analog circuitry or enjoy math but do not like physics.They apply this same decision threshold to evaluating career options.If an individual tries an internship in a technical field and decides that he/she did not like it, the student may extrapolate this small data sample and think that it means he/she will not like any job in that field and subsequently change majors.Here we are, individuals who are good at math, science, and scientific discovery, yet when it comes to our own careers, we do not use the same rigorous methods to determine our future.It is time to change all this.It is time to get comfortable being uncomfortable.My first part-time job was working in a dry cleaner.My tasks were to take in customers' dirty clothes, check the pockets to be sure they were empty, and then tag and track the clothes.Back then, we did not have health protection protocols in place, such as wearing gloves, to protect us from the horrible surprises that people forgot and left in their pockets.It was this experience that sent me running into engineering school.I decided that cleaning toilets with my toothbrush would be

This paper introduces a new parametric n-ary Gray code, the (n, k, p)-Gray code, which includes several commonly used codes such as the binary-reflected, ternary, and (n, k)-Gray codes. The new (n, k, p)-Gray code has potential applications in digital communications and signal/image processing systems. This paper focuses on three illustrative applications of the (n, k, p)-Gray code, namely, image bit-plane decomposition, image denoising, and encryption. The computer simulations demonstrate that the (n, k, p)-Gray code shows better performance than other traditional Gray codes for these applications in image systems.

Image enhancement is a crucial pre-processing step for various image processing applications and vision systems. Many enhancement algorithms have been proposed based on different sets of criteria. However, a direct multi-scale image enhancement algorithm capable of independently and/or simultaneously providing adequate contrast enhancement, tonal rendition, dynamic range compression, and accurate edge preservation in a controlled manner has yet to be produced. In this paper, a multi-scale image enhancement algorithm based on a new parametric contrast measure is presented. The parametric contrast measure incorporates not only the luminance masking characteristic, but also the contrast masking characteristic of the human visual system. The formulation of the contrast measure can be adapted for any multi-resolution decomposition scheme in order to yield new human visual system-inspired multi-scale transforms. In this article, it is exemplified using the Laplacian pyramid, discrete wavelet transform, stationary wavelet transform, and dual-tree complex wavelet transform. Consequently, the proposed enhancement procedure is developed. The advantages of the proposed method include: 1) the integration of both the luminance and contrast masking phenomena; 2) the extension of non-linear mapping schemes to human visual system inspired multi-scale contrast coefficients; 3) the extension of human visual system-based image enhancement approaches to the stationary and dual-tree complex wavelet transforms, and a direct means of; 4) adjusting overall brightness; and 5) achieving dynamic range compression for image enhancement within a direct multi-scale enhancement framework. Experimental results demonstrate the ability of the proposed algorithm to achieve simultaneous local and global enhancements.

In this paper, we introduce two novel edge detection algorithms based on a negative alpha weighted quadratic filter. The goal of this work is to utilize the characteristics of the nonlinear filter to preserve and enhance edges for the purpose of edge detection. Unlike traditional edge detection algorithms, which detect edges by using derivatives, the proposed algorithms operate on local regions and modify the color tones of uniform regions while preserving the original edges. We also incorporate the luminance masking feature of the Human Visual System by masking the gradient image before edge labeling. Experimental simulations show that the proposed algorithms can extract fine edge information from images contaminated by noise and affected by non-uniform illumination; the obtained edge maps are more consistent to the edges perceived by the human eye. Comparison with existing algorithms will be also presented.

Image fusion algorithms combine images obtained using different sensors into a single image to provide contextual enhancement of the scene being observed. The fusion of images obtained using infrared (IR) and visible light (VL) cameras are particularly appealing for security applications such as surveillance and concealed weapon detection. In this paper, a new image fusion algorithm is proposed, which considers both the luminance masking feature of the human visual system (HVS) and the nature of the pertinent information in IR images in the context of surveillance applications. Experimental results illustrate the improved performance of the proposed algorithm by both qualitative and quantitative means.

This paper presents a novel modified multi scale contrast enhancement (MMCE) technique for color image enhancement based on manipulating the DCT coefficients. Modified multi contrast enhancement (MCE) is an improved version of multi contrast enhancement by redefining the frequency spectral bands and introducing more band enhancement techniques to achieve a better performance. This paper also uses an image contrast measure SDME to choose the optimal parameters and to demonstrate the effectiveness of the methods. Computer simulations and analysis are shown that the presented method outperforms the commonly used methods such as Retinex and the original MCE for most images.

This paper introduces a new unsharp masking (UM) scheme, called nonlinear UM (NLUM), for mammogram enhancement. The NLUM offers users the flexibility 1) to embed different types of filters into the nonlinear filtering operator; 2) to choose different linear or nonlinear operations for the fusion processes that combines the enhanced filtered portion of the mammogram with the original mammogram; and 3) to allow the NLUM parameter selection to be performed manually or by using a quantitative enhancement measure to obtain the optimal enhancement parameters. We also introduce a new enhancement measure approach, called the second-derivative-like measure of enhancement, which is shown to have better performance than other measures in evaluating the visual quality of image enhancement. The comparison and evaluation of enhancement performance demonstrate that the NLUM can improve the disease diagnosis by enhancing the fine details in mammograms with no a priori knowledge of the image contents. The human-visual-system-based image decomposition is used for analysis and visualization of mammogram enhancement.

Image processing technologies such as image enhancement generally utilize linear arithmetic operations to manipulate images. Recently, Jourlin and Pinoli successfully used the logarithmic image processing (LIP) model for several applications of image processing such as image enhancement and segmentation. In this paper, we introduce a parameterized LIP (PLIP) model that spans both the linear arithmetic and LIP operations and all scenarios in between within a single unified model. We also introduce both frequency- and spatial-domain PLIP-based image enhancement methods, including the PLIP Lee's algorithm, PLIP bihistogram equalization, and the PLIP alpha rooting. Computer simulations and comparisons demonstrate that the new PLIP model allows the user to obtain improved enhancement performance by changing only the PLIP parameters, to yield better image fusion results by utilizing the PLIP addition or image multiplication, to represent a larger span of cases than the LIP and linear arithmetic cases by changing parameters, and to utilize and illustrate the logarithmic exponential operation for image fusion and enhancement.

Image fusion is the process of combining multiple images into a single image which retains the most pertinent information from each original image source. More recently, multi-scale image fusion approaches have emerged as a means of providing a more meaningful fusion which better reflects the human visual system. In this paper, multi-scale decomposition techniques and image fusion algorithms are adapted using the Parameterized Logarithmic Image Processing (PLIP) model, a nonlinear image processing framework which more accurately processes images. Experimental results via computer simulations illustrate the improved performance of the proposed algorithms by both qualitative and quantitative means.

This paper presents a multi tier web architecture that integrates web technology, Database system, and Batch processing tools for the development of a real time threat detection system. Four data repository models are introduced for effective data storage and retrieval. The baseline feature vectors are introduced and stored in the database table using a batch job. The batch job performs load balancing by calculating the new feature vector using the offline server and updates the online database server. The illustrative application uses the Hierarchical Multi level HVS segmentation, ratio based edge detection, and support vector machine for threat recognition and detection. The 64 bit edge based feature vector is generated for the baseline images and the input test object images using the cell edge distribution approach. The experimental results demonstrate that the presented framework is efficient in facilitating accurate threat detection and support the development of portable, reusable and scalable object recognition applications for heterogeneous distributed environment.

This paper introduces a new enhancement algorithm for prostate MR images using a new nonlinear filtering operation and an alpha-trimmed Mean Separation. A new enhancement measure is also introduced to measure and assess the enhanced results. Experimental results show that the presented algorithm can significantly improve the contrast of prostate MR images. It has a potential application in prostate cancer detection.

This paper introduces a new concept of Boolean derivatives as a fusion of partial derivatives of Boolean functions (PDBFs). Three efficient algorithms for the calculation of PDBFs are presented. It is shown that Boolean function derivatives are useful for the application of identifying the location of edge pixels in binary images. The same concept is extended to the development of a new edge detection algorithm for grayscale images, which yields competitive results, compared with those of traditional methods. Furthermore, a new measure is introduced to automatically determine the parameter values used in the thresholding portion of the binarization procedure. Through computer simulations, demonstrations of Boolean derivatives and the effectiveness of the presented edge detection algorithm, compared with traditional edge detection algorithms, are shown using several synthetic and natural test images. In order to make quantitative comparisons, two quantitative measures are used: one based on the recovery of the original image from the output edge map and the Pratt's figure of merit.

Edge detection has been used extensively as a preprocessing step for many computer vision tasks. Due to its importance in image processing and the highly subjective nature of human evaluation and visual comparison of edge detectors, it is desirable to formulate objective edge map evaluation measures. One would like to use such a measure to make comparisons of results using the same edge detector with different parameters as well as to make comparisons of results using different edge detectors. Reconstruction-based measures have the clear advantage that they effectively incorporate original image data. In this paper, a general model for reconstruction-based measures is established in order to alleviate the shortcomings of the reconstruction-based measures, followed by the formulation of a new non-reference measure for objective edge map evaluation. Experimental results illustrate the effectiveness of the new measure both as a means of selecting optimal edge detector parameters and as a means of determining the relative performance of edge detectors for a given image.

This paper introduces a new concept for image encryption using a binary ¿key-image¿. The key-image is either a bit plane or an edge map generated from another image, which has the same size as the original image to be encrypted. In addition, we introduce two new lossless image encryption algorithms using this key-image technique. The performance of these algorithms is discussed against common attacks such as the brute force attack, ciphertext attacks and plaintext attacks. The analysis and experimental results show that the proposed algorithms can fully encrypt all types of images. This makes them suitable for securing multimedia applications and shows they have the potential to be used to secure communications in a variety of wired/wireless scenarios and real-time application such as mobile phone services.

Summary form only given. Significant work has been performed on encrypting images and compressing images as two separate problems, but traditional encryption techniques generally degrade the compression ratio. To circumvent these issues, two methods have been used. The first employs known encryption algorithms on compressed image data. The second develops compression algorithms which work well for encrypted data. The contribution of this paper is using alpha rooting to perform simultaneous compression and encryption. This achieves improved compression performance in terms of computational complexity and compression ratio. Results are shown for 2 of the well known benchmark images, using the well known JPEG image compression standard to demonstrate the effectiveness of alpha rooting for simultaneous encryption and compression.

Extraction of the edges is a key step in image processing and there is still a continuing research effort to develop new and effective edge detection algorithms. Despite this fact, there is no single, reliable and efficient metric to evaluate the quality of an edge detector. We introduce an original method for image reconstruction that leads to edge evaluation based on image estimation. A new quantitative metric for assessment of the performance of the edge detector is also presented. The operation of the measure is established on a diverse image database using standard edge detection algorithms and the one based on partial derivatives of Boolean functions. The uses of the measure for an assortment of purposes are demonstrated and these are backed by visual assessment as well as some distance-based error functions applied on synthetic images.

Feature extraction is arguably the most important stage of an automatic object detection system. It is in this stage where the results of previous processing steps are interpreted to somehow characterize an object. Developing methods for feature extraction and feature vector generation using information from edge maps is a natural progression, as edge detection determines structure in images. A new edge-based feature extraction scheme is introduced based on the feature fusion of two existing methods. A generalized set of kernels for edge detection is also presented. The experimental results show that the detection of different objects of interests is improved using the new method.

In this paper we present a novel image quality measure for color images. This measure is based on the correlation of information between color planes and principal component analysis conversion of a color image into grayscale. Furthermore, a scheme for boundary detection based on this principle is developed and presented. This innovative design utilizes partial derivatives of Boolean functions for edge detection and is then analyzed using our new measure. Results testing the scheme on a database of natural and synthetic images show the performance to be quantitatively competitive with existing measures.

Objective assessment of image quality is important for a number of image processing applications. Similarity metrics have been used for methods such as automating compression, automating watermarking, and benchmarking algorithm success. The goal of objective quality assessment is to quantify the quality of images in a manner consistent with human perception. For this reason, we introduce a novel image similarity metric based on the human visual system. The measures of enhancement (EME, AME, and LogAME) have been successfully used to quantify human quality perception for image enhancement. In this paper, we present a modified version of the Logarithmic AME which can successfully be used to quantify image similarity. We compare the quantitative assessments of this algorithm with those of the well known Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) on the basis of correlation with subjective human evaluations for a number of images.

Image scrambling is an effective method for providing image security. This paper compares and discusses the effectiveness of some image scrambling algorithms based on recursive sequences such as the fibonacci number, generalized Fibonacci number, gray code, generalized gray code, generalized P-gray code, P-Fibonacci, P-Lucas, P-recursive sequences, and parametric M-sequences for image scrambling. The comparison of these methods for image security is based on three basic types of attacks: data loss attacks, noise attacks and plaintext attacks. The experimental results demonstrate that the scrambling algorithms based on both P-Fibonacci and P-Lucas sequences show better performance when subjected to attacks and also in terms of algorithm execution analysis which shows implementation efficiency and low computational requirements. This makes them suitable for real-time applications.

This paper presents a new concept of removing impulse noise through primary implicant elimination (PIE) applied to a logical system representation of the data. Applicable to binary and grayscale images, errors are corrected efficiently, in terms of the number of computations and memory requirements, while the fine details of the image are mostly preserved. Three filtering algorithms are presented: a general form in addition to iterative and switching variations. Experimental results on salt-and-pepper impulse noise, as well as on random-valued impulse noise, are compared against the performance of traditional median-based filters (both regular and switching) and are shown to be most successful in the often difficult case of when the original image contains many detailed patterns.

Varying scene illumination poses many challenging problems for machine vision systems. One such issue is developing global enhancement methods that work effectively across the varying illumination. In this paper, we introduce two novel image enhancement algorithms: edge-preserving contrast enhancement, which is able to better preserve edge details while enhancing contrast in images with varying illumination, and a novel multihistogram equalization method which utilizes the human visual system (HVS) to segment the image, allowing a fast and efficient correction of nonuniform illumination. We then extend this HVS-based multihistogram equalization approach to create a general enhancement method that can utilize any combination of enhancement algorithms for an improved performance. Additionally, we propose new quantitative measures of image enhancement, called the logarithmic Michelson contrast measure (AME) and the logarithmic AME by entropy. Many image enhancement methods require selection of operating parameters, which are typically chosen using subjective methods, but these new measures allow for automated selection. We present experimental results for these methods and make a comparison against other leading algorithms.

Images that do not have uniform brightness pose a challenging problem for image enhancement systems. As histogram equalization has been successfully used to correct for uniform brightness problems, we propose a new histogram equalization method that utilizes human visual system based thresholding as well as logarithmic processing techniques. Whereas previous histogram equalization methods have been limited in their ability to enhance these images, we demonstrate the effectiveness of this new method by enhancing a range of images with shadowing effects and inconsistent illumination. The images shown include images captured professionally and with cell phone cameras. Comparison with other methods are presented.

This paper introduces coordinate logic (CL) transforms as an alternative method for calculating coordinate logic (CL) filters. Additionally, a new measure and detection technique are introduced, enhancing the capabilities of the basic CL transform for the application of detecting edges within 2D signals (images). Applicable to binary and grayscale images, computer simulations demonstrate the success of this improved procedure on two classes of signals: synthetic (edge maps are know) and natural (edge maps are unknown). Results are evaluated quantitatively (via Pratt's figure of merit) and compared visually to two common edge detection techniques.

In real world machine vision problems, issues such as noise and variable scene illumination make edge and object detection difficult. There exists no universal edge detection method which works under all conditions. In this paper, we propose a logarithmic edge detection method. This achieves a higher level of scene illumination and noise independence. We present experimental results for this method, and compare results of the algorithm against several leading edge detection methods, such as Sobel and Canny. For an objective basis of comparison, we use Pratt's Figure of Merit. We further demonstrate the application of the algorithm in conjunction with Edge Detection based Image Enhancement (EDIE), showing that the use of this edge detection algorithm results in better image enhancement, as quantified by the Logarithmic AME measure.

Many applications of histograms for the purposes of image processing are well known. However, applying this process to the transform domain by way of a transform coefficient histogram has not yet been fully explored. This paper proposes three methods of image enhancement: a) logarithmic transform histogram matching, b) logarithmic transform histogram shifting, and c) logarithmic transform histogram shaping using Gaussian distributions. They are based on the properties of the logarithmic transform domain histogram and histogram equalization. The presented algorithms use the fact that the relationship between stimulus and perception is logarithmic and afford a marriage between enhancement qualities and computational efficiency. A human visual system-based quantitative measurement of image contrast improvement is also defined. This helps choose the best parameters and transform for each enhancement. A number of experimental results are presented to illustrate the performance of the proposed algorithms.

This paper examines the process of detecting edges using partial derivatives of Boolean functions in noisy multimedia environments. Applicable to binary and multi-bit (grayscale) data, this novel approach examines the binary representation of the original data to reconstruct edge locations within the scene. Competitive with traditional detectors in noise-free situations, the strength is its ability to detect edges in corrupted signals with no need for pre-filtering. Evaluation is performed on several synthetic and natural 2D images corrupted with impulse noise

This paper presents an enhancement technique based upon a new application of histograms on transform domain coefficients called logarithmic transform coefficient histogram shifting (LTHS). A measure of enhancement based on contrast entropy is used as a tool for evaluating the performance of the proposed enhancement technique and for finding optimal values for variables contained in the enhancement. The algorithm's performance is compared quantitatively to classical histogram equalization using the aforementioned measure of enhancement. Experimental results are presented to show the performance of the proposed algorithm alongside classical histogram equalization.

This paper presents a new class of the "frequency domain"-based signal/image enhancement algorithms including magnitude reduction, log-magnitude reduction, iterative magnitude and a log-reduction zonal magnitude technique. These algorithms are described and applied for detection and visualization of objects within an image. The new technique is based on the so-called sequency ordered orthogonal transforms, which include the well-known Fourier, Hartley, cosine, and Hadamard transforms, as well as new enhancement parametric operators. A wide range of image characteristics can be obtained from a single transform, by varying the parameters of the operators. We also introduce a quantifying method to measure signal/image enhancement called EME. This helps choose the best parameters and transform for each enhancement. A number of experimental results are presented to illustrate the performance of the proposed algorithms.

This paper presents an advanced concurrent simulation technique for performing Multiple Stuck-at Fault Simulation based on Multiple Domain Concurrent and Comparative Simulation (MDCCS). It efficiently compresses multiple experiments in a single simulation and requires no pre-analysis of the circuit. In addition, MDCCS has a unique feature that allows experiments to interact with each other and spawn offspring experiments should new behaviors arise. MDCCS is based on discrete event concurrent simulation (CS) and gains efficiency by utilizing the similarity among experiments without resorting to parallel hardware. It provides a mechanism for managing the complexity of interactions such that the implementation of this methodology is both storage and CPU time efficient. The intention of this paper is to present the MDCCS framework and report on its effectiveness for digital logic fault simulation.

A methodology is presented for fault simulation of a system level diagnostic program involving large models (50000 to 200000 gates) and long test sequences. The accuracy of memory models and the interplay of fault insertion and fault selection with diagnostic program development are topics covered. It also details observation and statistical methods and tools used to investigate the operation of 'faulty machines' (the fault effects created by an individual fault source). Observation of individual faulty machines is critical to provide information about looping and erratic programs, violations to subprogram sequencing, etc. This methodology is a successful attempt to make fault simulation of system diagnostics feasible.>

A methodology is presented for fault-simulation of system level diagnostic programs involving large models (50000 to 200000 gates) and long test sequences. Accuracy of memory models, interplay of target faults with diagnostic program development, and creation of shorter diagnostics are topics covered. Observation and statistical methods and tools used to investigate the operation of the faulty machine within the diagnostic program are also presented. Observation of individual faulty machines is critical to provide information about looping and erratic programs, violations of subprogram sequencing, etc. This methodology makes fault simulation of system diagnostics feasible.>