Due to the existence of noise and spectral redundancies in hyperspectral images (HSIs), the band selection (BS) is highly required and can be achieved through the attention mechanism. However, existing BS methods fail to consider global interaction between the spectral information and spatial information in a nonlinear fashion. In this letter, we propose an end-to-end unsupervised dual-attention reconstruction network for BS (DARecNet-BS). The proposed network employs a dual-attention mechanism, i.e., position attention module (PAM) and channel attention module (CAM), to recalibrate the feature maps and subsequently uses a 3-D reconstruction network to restore the original HSI. This way, the long-range nonlinear contextual information in spectral and spatial directions is captured, and the informative band subset can be selected. Experiments are conducted on three well-known hyperspectral data sets, i.e., Indian Pines (IP), University of Pavia (UP), and Salinas (SA), to compare existing BS approaches, and the proposedDARecNet-BScan effectively select less redundant bands with comparable or better classification accuracy. The source code will be made publicly available athttps://github.com/ucalyptus/DARecNet-BS.

With the increasing popularity of augmented and virtual reality, retailers are now focusing more towards customer satisfaction to increase the amount of sales. Although augmented reality is not a new concept but it has gained much needed attention over the past few years. Our present work is targeted towards this direction which may be used to enhance user experience in various virtual and augmented reality based applications. We propose a model to change skin tone of a person. Given any input image of a person or a group of persons with some value indicating the desired change of skin color towards fairness or darkness, this method can change the skin tone of the persons in the image. This is an unsupervised method and also unconstrained in terms of pose, illumination, number of persons in the image etc. The goal of this work is to reduce the time and effort which is generally required for changing the skin tone using existing applications (e.g., Photoshop) by professionals or novice. To establish the efficacy of this method we have compared our result with that of some popular photo editor and also with the result of some existing benchmark method related to human attribute manipulation. Rigorous experiments on different datasets show the effectiveness of this method in terms of synthesizing perceptually convincing outputs.

Document image binarization, especially old handwritten documents, is a very important yet challenging task. There are various bottlenecks for binarizing historical documents due to different types of degradation present imultaneously such as back impression, ink bleed through, faded colours, and wear and tear of the writing media. We consider these degradation as various types of noise in the document image. Here we have proposed a 2D morphological network which consists of basic morphological operation like dilation and erosion to perform our targeted task. The network also includes linear combination of output from dilation and erosion operations. The aforementioned 2D morphological network is applied for image binarization, where the structuring elements (SEs) and the weights of the linear combination layer are learned through back-propagation. The proposed network has been evaluated on DIBCO 2017 and H-DIBCO 2018 and ISI-Letter dataset. Our results show more convincing as compared to the results of other state-of-the-art methods. Though the network is developed for old handwritten documents, it may be tuned to work for image processing task. The source code can be found here https://github.com/ranjanZ/ICDAR_Binarization.

In this paper, we propose a novel multiple pyramids based image inpainting method using local patch statistics and geometric feature-based sparse representation to maintain texture consistency and structure coherence. First, we approximate each patch in the target region (region to be inpainted) by statistically dominant local candidate patches to preserve local consistency. Then each approximated patch is refined by a sparse representation of candidate patches based on local steering kernel (LSK) feature to retain texture quality. We also propose a multiple pyramids based approach to generate several inpainted versions of the input image, one for each of the pyramids. Finally, we combine the inpainted images by gradient-based weighted average to produce the final inpainted image. This approach helps to maintain structure coherence and to remove artifacts which may appear in the inpainted images due to different initial scales of the individual pyramids. The proposed method is tested on a wide range of natural images for scratch and blob/object removal. We have presented both quantitative and qualitative comparison with the existing methods to demonstrate the superiority of the proposed method.

This study presents a novel ensemble classifier‐based off‐line handwritten word recognition system following a holistic approach. Here each handwritten word is recognised using two handcrafted features, namely (i) Arnold transform‐based feature that addresses local directional feature which depends on the stroke orientation distribution of cursive word and (ii) oriented curvature‐based feature which is basically the histogram of curvelet index and one machine generated feature using deep convolution neural network (DCNN). In this study, a new architecture of DCNN is proposed for handwritten word recognition. These features are recognised by three classifiers separately. Finally, the decision of three classifiers is combined to predict the ultimate word class level. To fuse the decision of individual classifiers, the authors have explored three strategies: (i) vote for strongest decision, (ii) vote for majority decision and (iii) vote for the sum of the decisions. The proposed handwritten word recognition system is tested on three handwritten word databases: (i) CENPARMI database, (ii) IAM database and (iii) ISIHWD database. The performance of the proposed system is promising and comparable to state‐of‐the‐art handwriting recognition systems.

This paper presents a Markov random field (MRF)-based image inpainting algorithm using patch selection from groups of similar patches and optimal patch assignment through joint patch refinement. In patch selection, a novel group formation strategy based on subspace clustering is introduced to search the candidate patches in relevant source region only. This improves patch searching in terms of both quality and time. We also propose an efficient patch refinement scheme using higher order singular value decomposition to capture underlying pattern among the candidate patches. This eliminates random variation and unwanted artifacts as well. Finally, a weight term is computed, based on the refined patches and is incorporated in the objective function of the MRF model to improve the optimal patch assignment. Experimental results on a large number of natural images and comparison with well-known existing methods demonstrate the efficacy and superiority of the proposed method.

In bad weather conditions like fog and haze, the particles present in the atmosphere scatter incident light in different directions. As a result, image taken under these conditions suffers from reduced visibility, lack of contrast, as a result, it appears colorless. Image dehazing method tries to recover a haze-free portrayal of the given hazy image. In this paper we propose a method that dehazes a given image by comparing various output patches with the original hazy version and choosing the best one. The comparison is performed by our proposed dehazed patch quality comparator based on Convolutional Neural Network (CNN). To select the best dehazed patch we employ binary search. Quantitative and qualitative evaluations show that our method achieves good results in most of the cases, and are, on an average, comparable with state-of-the-art methods.

PDF format of scanned document images is not searchable. OCR tries to remedy this adversity by converting document images into editable and searchable data, but it has its own limitations in presence of equations - both mathematical and chemical. OCR system for mathematical equation is already a major research area and has provided successful result. However, chemical equation segmentation has been a less ventured road. In this paper, we present a novel method for automated generation of searchable PDF format of segmented chemical equations from scanned document images by performing chemical symbol recognition and auto-correction of OCR output. We use existing OCR system, pattern recognition technique, contextual data analysis and a standard LaTeX package to generate the chemical equation in searchable PDF format. The effectiveness of the proposed method is verified through exhaustive testing on 234 document images.

This paper presents a sparse representation based image inpainting method using local patch analysis and geometric structure based feature extraction. In local patch analysis, we approximate the target region by weighted average of some local patches which are frequently occurred within a neighborhood. Local patch statistics is applied to find the most relevant neighbors for each target patch. Further we extract local steering kernel (LSK) based feature to preserve geometric structure and texture sharpness in the target region. The advantage of non local self similarity as redundancy of similar patches in natural images is introduced to find the candidate patches from the whole source region. Based on these local and non local prior information we propose a sparse representation framework for image inpainting. Our proposed method is tested on wide range of natural images. The experimental results show the superiority of the proposed method compared to some of the previous approaches.

Images taken under fog or haze have their visibility reduced due to the existence of aerosols in the atmosphere. Image dehazing methods try to recover haze-free versions of these images by removing the effect of haze. Methods proposed till now are exclusively for daytime scene images or for night-time scene. The method we propose here can dehaze an image independent of whether it was captured during the day or night. To achieve this we have relaxed the image formation model to take into account spatially varying atmospheric light that may be present in night-time images. We estimate the contributing airlights and also the patches that are affected by it. We then remove the effect of airlight to obtain haze-free image. We demonstrate the results obtained by using our method on day and night-time images. We have compared out results with that of recently reported methods and the results show the effectiveness of our method.

An electrical or an electronic circuit often contains special combinations of circuit symbols in the form of sub-circuits. Identification and analysis of these sub-circuits can substantially simplify the underlying topology of a circuit. This paper explains our maiden attempt towards topological simplification of a circuit by identifying two important classes of sub-circuits—one formed by symbols connected in series and another by symbols connected in parallel. Although a sub-circuit is usually formed with the same type of circuit symbols, our method can handle any combination, irrespective of the symbol types. The method, in general, is based on a novel histogram analysis, mathematical morphology, and geometric features during the symbol identification phase, and a set of adjacency lists representing the connectivity matrix during the analysis phase. The idea may be extended to match topologically similar but spatially different circuits using graph isomorphism. It may also be used for vectorization of circuit drawings utilizing the information on the segmented circuit elements and their connectivity matrices. We have tested the proposed method on a dataset containing 83 scanned images of a variety of electronic and electrical drawings. Some of the results are presented here to demonstrate its efficacy and robustness.

This work presents a binarization technique of map document images. It exploits an amalgam of global and local threshold approaches best suited for binarization of document images with complex background and overlapping objects in the foreground like maps. The proposed approach uses Distance Transform (DT) and Adaptive threshold. Initially a rough estimate of the map background is done using Distance Transform (DT). This is followed by an adaptive threshold operation to extract the foreground. The efficacy and accuracy of the proposed technique are also compared, using ICDAR 2009 -- DIBCO (Document Image Binarization Contest) evaluation parameters, with other algorithms already reported in the literature.

In this paper, we present an exemplar-based image in painting technique using the higher order singular value decomposition (HOSVD). The two main steps of the proposed method are determination of patch priority and patch completion. Here we adopt gradient-based priority term. For patch completion, we build a stack of the candidate patches corresponding to the target patch. Then we find the coefficients matrix of singular values using HOSVD transformation from the stack and nullify some singular values which corresponds to some artifacts. Next, we invert the HOSVD transform and synthesize the target patch by taking weighted average of filtered candidate patches. We also incorporate local patch consistency in the proposed model. Experimental results show the superiority of the proposed method compared to the competitive methods. The proposed method may be used for restoration of digital images of defective or damaged artifacts.

Due to occlusion, lighting condition, variation in clothing dance video classification is a challenging problem in computer vision domain. In this paper we present a local spatiotemporal feature model on manifold for Indian Classical Dance (ICD) classification. We represent features at each space-time interest point as a covariance matrix by fusing different order spatial and temporal derivatives. Each video clip is then represented in bag-of-words framework on manifold using Jensen-Bregman LogDet Divergence. Classification is done by popular non-linear SVM with ?2-kernel. We evaluate our system on a ICD dataset created from YouTube and get 69.39% accuracy which is better than that of the state-of-the-art human activity classification algorithms. We have also tested our algorithms on human activity benchmark datasets like KTH, and UCF50 and get promising results compared to the state-of-the-art methods.

In this paper, a novel fuzzy rule-based prediction framework is developed for high-quality image zooming. In classical interpolation-based image zooming, resolution is increased by inserting pixels using certain interpolation techniques. Here, we propose a patch-based image zooming technique, where each low-resolution (LR) image patch is replaced by an estimated high-resolution (HR) patch. Since an LR patch can be generated from any of the many possible HR patches, it would be natural to develop rules to find different possible HR patches and then to combine them according to rule strength to get the estimated HR patch. Here, we generate a large number of LR–HR patch pairs from a collection of natural images, group them into different clusters, and then generate a fuzzy rule for each of these clusters. The rule parameters are also learned from these LR-HR patch pairs. As a result, an efficient mapping from LR patch space to HR patch space can be formulated. The performance of the proposed method is tested on different images, and is also compared with other representative as well as state-of-the-art image zooming techniques. Experimental results show that the proposed method is better than the competing methods and is capable of reconstructing thin lines, edges, fine details, and textures in the image efficiently.

This paper presents a novel space-time feature-based human activity analysis system. We detect Space Time Interest Points (STIP) and generate their description based on the facet model. The proposed approach detects interest points in video data using the three-dimensional facet model efficiently. Then we describe each interest point by three-dimensional Haar wavelet transform and time derivatives of different order obtained from said facet model. Here we represent each video clip following the bag-of-words approach by learning feature specific dictionary. Finally, classification is done using non-linear SVM with χ2-kernel. We evaluate the performance of our system on standard datasets like Weizmann, KTH, UCF sports, ICD, UCF YouTube, and UCF50 and get better, or at least comparable results compared to other state-of-the-art systems.

We propose a novel high-quality image zooming technique using fuzzy rule based prediction framework. Our approach is based on the fundamental idea that a low-resolution (LR) image patch could be generated from any of the many possible high-resolution (HR) image patches. Therefore it would be natural to assign certain fuzzyness to each of the possibilities of HR patches. We develop a prediction system that learns the LR-HR patch correspondence and also the natural image patch prior from an external database. We do so by collecting a large amount of the LR-HR natural image patch pairs from an existing database, grouping them into different clusters and then generating fuzzy rules to get an efficient mapping from LR patch space to HR patch space. Experimental results show the efficacy of our method over existing state-of-art methods.

To have a unique solution of an ill-posed inverse problem, the usual way is to embed prior information in terms of regularizer or smoothness criterion. In this work, both the inverse mechanism (the relationship of blur and sharp patches) and the smoothness prior are learned simultaneously from the image itself, in multiple scales. We have shown experimentally that the proposed method outperform the existing state-of-the-art techniques on high noise environment and produce comparable result otherwise; moreover, it is almost three times faster than existing ones.

In this paper, we address an interesting application of computer vision technique, namely classification of Indian Classical Dance (ICD). With the best of our knowledge, the problem has not been addressed so far in computer vision domain. To deal with this problem, we use a sparse representation based dictionary learning technique. First, we represent each frame of a dance video by a pose descriptor based on histogram of oriented optical flow (HOOF), in a hierarchical manner. The pose basis is learned using an on-line dictionary learning technique. Finally each video is represented sparsely as a dance descriptor by pooling pose descriptor of all the frames. In this work, dance videos are classified using support vector machine (SVM) with intersection kernel. Our contribution here are two folds. First, to address dance classification as a new problem in computer vision and second, to present a new action descriptor to represent a dance video which overcomes the problem of the “Bags-of-Words” model. We have tested our algorithm on our own ICD dataset created from the videos collected from YouTube. An accuracy of 86.67% is achieved on this dataset. Since we have proposed a new action descriptor too, we have tested our algorithm on well known KTH dataset. The performance of the system is comparable to the state-of-the-art.

Alteration and addition to valuable data on paper documents are among the fastest growing crimes around the globe. The loss due to these crimes is huge and is increasing with an alarming rate. The techniques, which are used by forensic document examiners, to examine such cases are still limited to manual examination of physical, chemical and microscopic characteristics. Moreover, it is very difficult to detect an alteration when the ink of similar color is involved. We could not find much in the literature to deal with this problem in an automated pattern recognition framework. In this paper, we restrict ourselves to alterations made with ball-point pen strokes and propose a scheme for detection of such alterations using pattern recognition tools. For this, a large set of color and texture based features is extracted. To choose an adequate set of useful features from the extracted ones, a multilayer perceptron (MLP)-based feature analysis technique is used. For detection of the alteration, three different classifiers, namely, K-nearest neighbor, MLP and support vector machines are used. The results are quite promising.

Multiscale morphological operators are studied extensively in the literature for image processing and feature extraction purposes. In this paper, we model a nonlinear regularization method based on multiscale morphology for edge-preserving super resolution (SR) image reconstruction. We formulate SR image reconstruction as a deblurring problem and then solve the inverse problem using Bregman iterations. The proposed algorithm can suppress inherent noise generated during low-resolution image formation as well as during SR image estimation efficiently. Experimental results show the effectiveness of the proposed regularization and reconstruction method for SR image.

We have presented a unified model for detecting different types of video shot transitions. Based on the proposed model, we formulate frame estimation scheme using the previous and the next frames. Unlike other shot boundary detection algorithms, instead of properties of frames, frame transition parameters and frame estimation errors based on global and local features are used for boundary detection and classification. Local features include scatter matrix of edge strength and motion matrix. Finally, the frames are classified as no change (within shot frame), abrupt change, or gradual change frames using a multilayer perceptron network. The proposed method is relatively less dependent on user defined thresholds and is free from sliding window size as widely used by various schemes found in the literature. Moreover, handling both abrupt and gradual transitions along with non-transition frames under a single framework using model guided visual feature is another unique aspect of the work.

Text line segmentation is one of the important steps for offline handwritten text / handwriting recognition. This paper describes a novel method of text line segmentation based on the physical process of writing. The basic concept behind this segmentation method is: two successive handwritten text-line are always non-intersecting. The proof of the theory is explained with a model of pen-tip movement. The line segmentation is done by arranging the centroids of connected components present in the given text document. Experiments show that the proposed method achieves high accuracy for detecting unconstrained text line of handwritten documents.

This paper proposes a technique for automatic recognition of Bengali handwritten numerals using multiple feature sets. We discuss about some novel Morphological features and k-curvature feature extraction technique to recognize handwritten scripts. We use different multi-layer perceptron (MLP) classifiers to train this feature spaces and then fuse those classifiers using modified `Naive'-Bayes combination to increase accuracy of recognition result. The individual feature sets give reasonably high accuracy up-to 96.25%, while fused classifier gives accuracy of 97.75%.

Linking a person based on handwritten documents is one of the oldest techniques that is used by crime investigators and forensic scientists. The importance of writer recognition in anthrax letter cases has made this examination popular in recent years. In this paper we propose four feature set namely directional opening, directional closing, direction erosion and k-curvature features for writer recognition on Telugu handwritten documents. Each of the features is extracted from the words after dividing them into a number of cells and then subjected to a nearest neighbor classifier for writer recognition. Although the results of each of the feature set is quite encouraging, the directional opening feature outperforms other feature sets.

Text within a camera grabbed image can contain a huge amount of meta data about that scene. Such meta data can be useful for identification, indexing and retrieval purposes.Detection of colored scene text is a new challenge for all camera based images.Common problems for text extraction from camera based images are the lack of prior knowledge of any kind of text features such as color, font, size and orientation.In this paper we propose a new algorithm for the extraction of text from an image which can overcome these problems. In addition, problems due to an unconstrained complex background in the scene has also been addressed.Here a new technique is applied to determine the discrete edges around the text boundaries. A novel methodology is also proposed to extract the text exploiting its appearance in terms of color and spatial distribution.

An Independent Component Analysis (ICA)-based image enhancement technique is presented to improve the accuracy for machine reading of camera-based images. Images of inscriptions that are normally engraved on stones or other durable materials and found at the sites of historical monuments are taken as a reference for conducting the present experiments. Significant improvement in recognition rate of a commercial OCR system shows the potential of the proposed ICA-based method. It improves word and character recognition accuracies of the OCR system by 68.6% (from 11.2% to 79.8%) and 57.3% (from 34.8% to 92.1%), respectively.

For efficient indexing, browsing and retrieval of video data and also for video summarization, extraction of representative frames is essential. Once a video stream is segmented into shots, the representative frames or key-frames for the shot are selected. Automatic selection of suitable representatives for a wide variety of shots is still a challenge as the number of such frames in a shot may also vary depending on the variation in the content. In this work, we propose a novel scheme that relies on Wald-Wolfowitz runs test based hypothesis testing to detect the subshots within a shot and then for each subshot, the frame rendering the highest fidelity is extracted as the key-frame. Experimental result shows that the scheme works satisfactorily for a wide variety of shots.

Text, graphics and half-tones are the major constituents of any document page. While half-tone can be characterised by its inherent intensity variation, text and graphics share common characteristics except difference in spatial distribution. The success of document image analysis systems depends on the proper segmentation. The success of document image analysis systems depends on the proper segmentation of text and graphics as text is further subdivided into other classes such as heading, table and math-zones. Segmentation of graphics is essential for better OCR performance and vectorization in computer vision applications. Graphics segmentation from text is particularly difficult in the context of graphics made of small components (dashed or dotted lines etc.) which have many features similar to texts. Here we propose a robust technique for segmenting all sorts of graphics and texts in any orientation from document pages.

Kohonen's SOM algorithm has been used successfully by some researchers for designing codebooks. However, while performing an exhaustive search in a large codebook with high dimensional vectors, the encoder faces a significant computational barrier. Due to its topology preservation property, SOM holds a good promise of being utilized for fast codebook searching. In this paper we develop a method for fast codebook searching by exploiting the topology preservation property of SOM. This method performs non-exhaustive search of the codebook to find a good match for a input vector. The method is a general one that can be applied to various signal domains. In the present paper its efficacy is demonstrated with spatial vector quantization of gray-scale images.

In this paper we propose a fully automatic hierarchical method for identification of forms using global as well as local features. Moments of certain orders are considered as global shape features and are utilised to reduce the search space by selecting a subset of forms present in the database. The type of the candidate form is then identified within this subset through detail analysis using local geometrical and topological features. The candidate form is then segmented to extract the user-filled information.

We propose a new scheme of designing a vector quantizer for image compression. First, a set of codevectors is generated using the self-organizing feature map algorithm. Then, the set of blocks associated with each code vector is modeled by a cubic surface for better perceptual fidelity of the reconstructed images. Mean-removed vectors from a set of training images is used for the construction of a generic codebook. Further, Huffman coding of the indices generated by the encoder and the difference-coded mean values of the blocks are used to achieve better compression ratio. We proposed two indices for quantitative assessment of the psychovisual quality (blocking effect) of the reconstructed image. Our experiments on several training and test images demonstrate that the proposed scheme can produce reconstructed images of good quality while achieving compression at low bit rates. Index Terms-Cubic surface fitting, generic codebook, image compression, self-organizing feature map, vector quantization.

With an aim to high-level understanding of the mathematicalcontents in a document image the requirement ofmath-zone extraction and recognition technique is obvious.In this paper we present fully auotmatic segmentation ofdisplayed-math zones from the document image, using onlythe spatial layout information of math-formulas and equations,so as to help commercial OCR systems which cannotdiscern math-zones and also for the identification and arrangementof math symbols by others.

With an aim to extract the structural information from the table of contents (TOC) to help develop a digital document library, the requirement of identifying/segmenting the TOC page is obvious. The objective to create a digital document library is to provide a non-labour intensive, cheap and flexible way of storing, representing and managing the paper document in electronic form to facilitate indexing, viewing, printing and extracting the intended portions. Information from the TOC pages is to be extracted for use in a document database for effective retrieval of the required pages. We present a fully automatic identification and segmentation of a table of contents (TOC) page from a scanned document.

How to select a structuring element for a given task is one of the most frequently asked questions in morphology. The present work tries to find a solution for a restricted class of problems, in the domain of shape classification. In this work an algorithm that extracts distinctive structure of each of a given set of objects, which can be used as the structuring elements for object classification system employing the hit-and-miss transformation, is proposed. The proposed algorithm is based on a new measure of local shape property. The method is used to develop a system for Bengali numeral recognition.

In this paper, the authors have proposed a method of segmenting gray level images using multiscale morphology. The approach resembles the watershed algorithm in the sense that the dark (respectively bright) features which are basically canyons (respectively mountains) on the surface topography of the gray level image are gradually filled (respectively clipped) using multiscale morphological closing (respectively opening) by reconstruction with isotropic structuring element. The algorithm detects valid segments at each scale using three criteria namely growing, merging and saturation. Segments extracted at various scales are integrated in the final result. The algorithm is composed of two passes preceded by a preprocessing step for simplifying small scale details of the image that might cause over-segmentation. In the first pass feature images at various scales are extracted and kept in respective level of morphological towers. In the second pass, potential features contributing to the formation of segments at various scales are detected. Finally the algorithm traces the contours of all such contributing features at various scales. The scheme after its implementation is executed on a set of test images (synthetic as well as real) and the results are compared with those of few other standard methods. A quantitative measure of performance is also formulated for comparing the methods.

In this paper two connectionist models for mid-level vision problems, namely, edge and line linking, have been presented. The processing elements (PE) are arranged in the form of two-dimensional lattice in both the models. The models take the strengths and the corresponding directions of the fragmented edges (or lines) as the input. The state of each processing element is updated by the activations received from the neighboring processing elements. In one model, each neuron interacts with its eight neighbors, while in the other model, each neuron interacts over a larger neighborhood. After convergence, the output of the neurons represent the linked edge (or line) segments in the image. The first model directly produces the linked line segments, while the second model produces a diffused edge cover. The linked edge segments are found by finding out the spine of the diffused edge cover. The experimental results and the proof of convergence of the network models have also been provided.>

An image segmentation scheme based on gray-level thresholding is presented. To reduce errors in misclassification, gray-level histograms are sharpened before thresholding using a gray-level transformation function that also leads to an expression for computing the expected threshold. Three new thresholding methods are proposed that reduce the noise, smooth region boundaries, and preserve connectedness among different parts of objects, and are not expensive.>

A differentiation/enhancement edge detector for noisy situations is proposed. The definition of the gradient has been initiated from the algorithm for finding a border in a binary picture. The advantages of this operator have been compared with those of other widely used operators. A measurement of error in extracting edges by thresholding the gradient has also been suggested, and for the detection of acceptable edges, the optimum threshold is chosen corresponding to the minima in the error function.

A linear degradation model for digital image restoration is considered. It is assumed that the noise is additive, uncorrelated and independent of the degradation process and also of original image. A new objective criterion is used for the least square restoration method proposed here. The criterion is to minimize the correlation between image and noise gray levels. The method can be implemented in the Fourier domain and is computationally attractive. The test results on a chromosome picture are demonstrated for different signal-to-noise power ratios.