Pixel-level fusion of visible and infrared images has demonstrated promise in enhancing information representation. However, nighttime image fusion remains challenging due to low and uneven lighting. Existing fusion methods neglect the preservation of color-related information at night, resulting in unsatisfactory outcomes with insufficient brightness. This paper presents a novel color image fusion framework to prevent color distortion, thus generating results more aligned with human perception. Firstly, we design an image fusion network to retain color information from visible images under low-light conditions. Secondly, we incorporate mature low-light enhancement technology into the network as a flexible component to produce fusion results under normal illumination. The training process is carefully designed to address potential issues of overexposure or noise amplification. Finally, we utilize knowledge distillation to create a lightweight end-to-end network that directly generates fusion results under normal lighting conditions from pairs of low-light images. Experimental results demonstrate that our proposed framework outperforms existing methods in nighttime scenarios.

Unsupervised image restoration methods relying on a single data source often face challenges in achieving high-quality visual data completion due to the absence of additional supplementary information. This paper presents a novel optimization framework to address this limitation and further enhance the performance of image restoration. The framework generates pseudo side information (PSI) and utilizes it to guide the process of visual data completion. We introduce a pseudo side information regularizer (PSIR) tailored specifically for visual data completion tasks. The PSIR comprises two components: the PSI generator and updater, responsible for generating and refining the PSI, and the neural self-expressive prior (NSEP), which identifies a prior matching the desired result and PSI during optimization. Notably, our method achieves comprehensive visual data completion across various data types without the need for additional reference side information or training data. Extensive experimental evaluations conducted on spectral data (including color images, multispectral images, and hyperspectral images), video data (including gray video, color video, and hyperspectral video), magnetic resonance image, and real cloud data demonstrate the superiority of our approach over other state-of-the-art completion methods under different missing rate scenarios.

Hyperspectral image (HSI) denoising algorithms often face challenges with non-uniform, band-dependent noise and fail to fully utilize high signal-to-noise ratio (SNR) spectral bands. To address these issues, this paper presents a zero-shot framework that leverages high-SNR bands as scene-specific references to guide the restoration of degraded bands. The framework features a dual-branch network architecture with the guide branch extracting high-fidelity spatial structures from high-SNR bands and the denoising branch restoring degraded bands through dynamic feature fusion. Fusion modules, enhanced with spectral-spatial attention, enable adaptive and spatially aware refinement between branches. Unlike conventional methods relying on external training datasets or fixed priors, the proposed approach adapts to the scene-specific quality disparities within each HSI. Extensive experiments on synthetic and real-world datasets demonstrate its superior ability in preserving spatial-spectral fidelity and handling complex noise scenarios, outperforming state-of-the-art methods.

The fusion of infrared and visible imagery plays a crucial role in environmental monitoring. Existing approaches aim to achieve high-quality perceptual results for human observers and robust outcomes for machine-based high-level tasks by adopting a joint design of fusion and segmentation. However, design constraints imposed by predetermined fusion rules limit the precision of high-level tasks, and drawbacks in the image domains to be fused are often overlooked. This paper presents a novel semantic-powered infrared and visible image fusion framework to address these issues. The key feature of our approach is the utilization of trainable gains and weights in enhancement and fusion processes, influenced solely by segmentation and serving as uncertainty parameters. We propose a two-stage training strategy: initially, training a combined enhancement and fusion network with random uncertainty parameters, followed by the estimation of semantic-driven uncertainty parameters. The enhancement and fusion process is optimized within the Laplacian Pyramid framework to ensure efficient computation. Experimental results highlight the significance of modeling the fusion process with uncertainty for achieving satisfactory fusion and segmentation outcomes.

This paper presents a hyperspectral image (HSI) reconstruction technique based on physics-driven optimization of multispectral filter array (MSFA) patterns. The encoding of HSIs using an MSFA and their decoding through deep learning has gained increasing attention. However, previous studies have seldom explored pattern optimization from a physical perspective during the encoding process. In this paper, we apply a spectral sensitivity function (SSF) response model to generate the MSFA, and the goal of encoder optimization extends from SSF to physical structural parameters. To fully utilize spatial and spectral information in the decoding process, we design an end-to-end dual-branch spatial-spectral fusion network (DSFNet). By jointly optimizing the MSFA with the SSF response model and DSFNet, the proposed method significantly improves the reconstruction accuracy of HSI. When compared with existing HSI reconstruction methods, our proposed approach achieves state-of-the-art performance in both metric and visual quality.

This article presents U 2PNet, a novel unsupervised underwater image restoration network using polarization for improving signal-to-noise ratio and image quality in underwater imaging environments. Traditional methods for underwater image restoration using polarization require specific cues or pairs of underwater polarization datasets, which limit their practical applications. Our proposed method requires only one mosaicked polarized image of the scene and does not require datasets for pretraining or specific cues. We design two subnetworks (T-net and B textsubscript ∞ -net) to accurately estimate the transmission map and background light, and unique nonreference loss functions to ensure effective restoration. Our experiments are based on an indoor polarization simulated dataset and a real polarization image dataset constructed from our underwater robotic platform equipped with polarization cameras. Experiment results demonstrate that our proposed method achieves state-of-the-art performance on both simulated and real underwater polarization images. The code and datasets will be available at https://github.com/polwork/U-2Pnet.

This paper introduces a Polarization-Driven Solution (PDS) to enhance the contrast of underwater imagery degraded by light scattering and uneven illumination. Images taken in underwater environments suffer from reduced contrast due to the combined effects of light scattering and non-uniform illumination. We present an Underwater Joint Degradation Model (UJDM) that effectively describes the compounded impacts of scattering and uneven illumination. By exploiting the polarization distinctions between objects and scattered light, we mitigate the deleterious effects of light scattering. Additionally, we leverage the polarization information to persist across changes in illumination, enhancing contrast and detail, especially in darker image regions. Building upon this foundation, our proposed underwater image restoration technique synergistically combines de-scattering with low-light image enhancement. We establish a benchmark database comprising both simulated and authentic underwater polarization images. Experiment results demonstrate that our proposed technique outperforms state-of-the-art underwater image contrast enhancement algorithms, validated through both subjective assessments and objective evaluation metrics. The source code and the datasets are available at https://github.com/polwork/PDS.

Hyperspectral image (HSI) exhibits high-quality spectral signals that convey subtle differences, enabling the discrimination of similar materials and providing a unique advantage for anomaly detection (AD). Fine spectral of anomalies can be effectively identified amidst heterogeneous background pixels. Given the similarity of materials in spatial and spectral dimensions, joint utilization of spatial and spectral information enhances detection performance. However, many existing AD approaches for HSIs usually achieve high accuracy at the expense of high computational complexity. In response to the requirements of practical detection scenarios-efficiency, robustness, and accuracy-this article introduces a rapid and robust AD algorithm through discriminative band selection for HSIs. We propose a spatial-spectral feature extraction strategy to ensure detection accuracy. Initially, to effectively mine context information across a broad spectral range, the HSI cube in space is partitioned into several groups using a coarse-to-fine strategy. Subsequently, we identify the most relevant and informative bands based on spatial local density and spectral information entropy, forming the coarse HSI bands subset. Following this, we design a multiband target-background ratio (MBTBR) to capture strongly discriminative bands, resulting in the fine HSI bands subset. Finally, we present an adaptively spatial—spectral feature extraction strategy to detect anomalous targets. Extensive experimental results on real hyperspectral datasets demonstrate that the proposed method achieves satisfactory performance compared to the state-of-the-art algorithms, validating its strong robustness and low computational complexity simultaneously.

In industrial inspection scenarios, the scarcity of data and the varying appearances of anomalies pose significant challenges for existing methods in accurately localizing anomaly edges and reducing detection errors. To address these issues, we propose a few-sample anomaly detection method based on edge enhancement and cascade optimization of residual features. Our approach includes a distribution transformation-based augmentation method to generate a variety of augmented images that closely resemble the distribution of real anomaly images. We introduce an anomaly detection method combined with an edge-guided feature enhancement module and a complementary feature attention module, which accentuates features at the edges of anomalies and emphasizes anomalous regions in a cascaded structure to achieve refined anomaly localization. Extensive experiments on three widely used datasets demonstrate that the proposed approach outperforms state-of-the-art methods in detection and localization accuracy.

This paper presents a deep learning-based spectral demosaicing technique trained in an unsupervised manner. Many existing deep learning-based techniques relying on supervised learning with synthetic images, often underperform on real-world images, especially as the number of spectral bands increases. This paper presents a comprehensive unsupervised spectral demosaicing (USD) framework based on the characteristics of spectral mosaic images. This framework encompasses a training method, model structure, transformation strategy, and a well-fitted model selection strategy. To enable the network to dynamically model spectral correlation while maintaining a compact parameter space, we reduce the complexity and parameters of the spectral attention module. This is achieved by dividing the spectral attention tensor into spectral attention matrices in the spatial dimension and spectral attention vector in the channel dimension. This paper also presents Mosaic 25 , a real 25-band hyperspectral mosaic image dataset featuring various objects, illuminations, and materials for benchmarking purposes. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed method outperforms conventional unsupervised methods in terms of spatial distortion suppression, spectral fidelity, robustness, and computational cost. Our code and dataset are publicly available at https://github.com/polwork/Unsupervised-Spectral-Demosaicing.

The fusion of infrared and visible images combines the information from two complementary imaging modalities for various computer vision tasks. Many existing techniques, however, fail to maintain a uniform overall style and keep salient details of individual modalities simultaneously. This paper presents an end-to-end Laplacian Pyramid Fusion Network with hierarchical guidance (HG-LPFN) that takes advantage of pixel-level saliency reservation of Laplacian Pyramid and global optimization capability of deep learning. The proposed scheme generates hierarchical saliency maps through Laplacian Pyramid decomposition and modal difference calculation. In the pyramid fusion mode, all sub-networks are connected in a bottom-up manner. The sub-network for low-frequency fusion focuses on extracting universal features to produce an opposite style while sub-networks for high-frequency fusion determine how much the details of each modality will be retained. Taking the style, details, and background into consideration, we design a set of novel loss functions to supervise both low-frequency images and full-resolution images under the guidance of saliency maps. Experimental results on public datasets demonstrate that the proposed HG-LPFN outperforms the state-of-the-art image fusion techniques.

Extensive works have been reported in hyperspectral images (HSIs) and multispectral images (MSIs) fusion to raise the spatial resolution of HSIs. However, the limited acquisition of HSIs has been an obstacle to such approaches. Spectral super-resolution (SSR) of MSI is a challenging and less investigated topic, which can also provide high-resolution synthetic HSIs. To deal with this high ill-posedness problem, we perform super-resolution enhancement of MSIs in the spectral domain by incorporating a spectral library as a priori. First, an aligned spectral library, which maps the open-source spectral library to a specific spectral library created for the reconstructed HR HSI, is represented. An intermediate latent HSI is obtained by fusing the spatial information from MSI and the hyperspectral information from a specific spectral library. Then, we use low-rank attribute embedding to transfer latent HSI into a robust subspace. Finally, a low-rank HSI dictionary representing the hyperspectral information is learned from the latent HSI. The adaptive sparse coefficient of MSI is obtained with a nonnegative constraint. By fusing these two terms, we get the final HR HSI. The proposed SSR model does not require any pretraining stages. We confirm the validity and superiority of our proposed SSR algorithm by comparing it with several benchmark state-of-the-art approaches on different datasets.

Recently, tensor sparsity modeling has achieved great success in the tensor completion (TC) problem. In real applications, the sparsity of a tensor can be rationally measured by low-rank tensor decomposition. However, existing methods either suffer from limited modeling power in estimating accurate rank or have difficulty in depicting hierarchical structure underlying such data ensembles. To address these issues, we propose a parametric tensor sparsity measure model, which encodes the sparsity for a general tensor by Laplacian scale mixture (LSM) modeling based on three-layer transform (TLT) for factor subspace prior with Tucker decomposition. Specifically, the sparsity of a tensor is first transformed into factor subspace, and then factor sparsity in the gradient domain is used to express the local similarity in within-mode. To further refine the sparsity, we adopt LSM by the transform learning scheme to self-adaptively depict deeper layer structured sparsity, in which the transformed sparse matrices in the sense of a statistical model can be modeled as the product of a Laplacian vector and a hidden positive scalar multiplier. We call the method as parametric tensor sparsity delivered by LSM-TLT. By a progressive transformation operator, we formulate the LSM-TLT model and use it to address the TC problem, and then the alternating direction method of multipliers-based optimization algorithm is designed to solve the problem. The experimental results on RGB images, hyperspectral images (HSIs), and videos demonstrate the proposed method outperforms state of the arts.

Existing methods for tensor completion (TC) have limited ability for characterizing low-rank (LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes hidden in a tensor, we propose a new multilayer sparsity-based tensor decomposition (MLSTD) for the low-rank tensor completion (LRTC). The method encodes the structured sparsity of a tensor by the multiple-layer representation. Specifically, we use the CANDECOMP/PARAFAC (CP) model to decompose a tensor into an ensemble of the sum of rank-1 tensors, and the number of rank-1 components is easily interpreted as the first-layer sparsity measure. Presumably, the factor matrices are smooth since local piecewise property exists in within-mode correlation. In subspace, the local smoothness can be regarded as the second-layer sparsity. To describe the refined structures of factor/subspace sparsity, we introduce a new sparsity insight of subspace smoothness: a self-adaptive low-rank matrix factorization (LRMF) scheme, called the third-layer sparsity. By the progressive description of the sparsity structure, we formulate an MLSTD model and embed it into the LRTC problem. Then, an effective alternating direction method of multipliers (ADMM) algorithm is designed for the MLSTD minimization problem. Various experiments in RGB images, hyperspectral images (HSIs), and videos substantiate that the proposed LRTC methods are superior to state-of-the-art methods.

We propose a novel graph Laplacian-guided coupled tensor decomposition (gLGCTD) model for fusion of hyperspectral image (HSI) and multispectral image (MSI) for spatial and spectral resolution enhancements. The coupled Tucker decomposition is employed to capture the global interdependencies across the different modes to fully exploit the intrinsic global spatial-spectral information. To preserve local characteristics, the complementary submanifold structures embedded in high-resolution (HR)-HSI are encoded by the graph Laplacian regularizations. The global spatial-spectral information captured by the coupled Tucker decomposition and the local submanifold structures are incorporated into a unified framework. The gLGCTD fusion framework is solved by a hybrid framework between the proximal alternating optimization (PAO) and the alternating direction method of multipliers (ADMM). Experimental results on both synthetic and real data sets demonstrate that the gLGCTD fusion method is superior to state-of-the-art fusion methods with a more accurate reconstruction of the HR-HSI.

Conventional tensor completion (TC) methods generally assume that the sparsity of tensor-valued data lies in the global subspace. The so-called global sparsity prior is measured by the tensor nuclear norm. Such assumption is not reliable in recovering low-rank (LR) tensor data, especially when considerable elements of data are missing. To mitigate this weakness, this article presents an enhanced sparsity prior model for LRTC using both local and global sparsity information in a latent LR tensor. In specific, we adopt a doubly weighted strategy for nuclear norm along each mode to characterize global sparsity prior of tensor. Different from traditional tensor-based local sparsity description, the proposed factor gradient sparsity prior in the Tucker decomposition model describes the underlying subspace local smoothness in real-world tensor objects, which simultaneously characterizes local piecewise structure over all dimensions. Moreover, there is no need to minimize the rank of a tensor for the proposed local sparsity prior. Extensive experiments on synthetic data, real-world hyperspectral images, and face modeling data demonstrate that the proposed model outperforms state-of-the-art techniques in terms of prediction capability and efficiency.

This paper presents a joint dehazing and denoising scheme for an image taken in hazy conditions. Conventional image dehazing methods may amplify the noise depending on the distance and density of the haze. To suppress the noise and improve the dehazing performance, an imaging model is modified by adding the process of amplifying the noise in hazy conditions. This model offers depth-chromaticity compensation regularization for the transmission map and chromaticity-depth compensation regularization for dehazing the image. The proposed iterative image dehazing method with polarization uses these two joint regularization schemes and the relationship between the transmission map and dehazed image. The transmission map and irradiance image are used to promote each other. To verify the effectiveness of the algorithm, polarizing images of different scenes in different days are collected. Different algorithms are applied to the original images. Experimental results demonstrate that the proposed scheme increases visibility in extreme weather conditions without amplifying the noise.

This paper proposes a novel method based on deformable dictionary learning for detecting the regions of change between multitemporal image pairs. We build on our previous work, which constructed a pair of dictionaries. The main shortcoming of this method was its dependence on a large amount of training data. In practice, there is often a shortage of ground-truthed training images, which limits the expression capability of the resulting dictionaries. This paper overcomes this challenge by incorporating the concept of deformation, wherein each atom of a dictionary is no longer a simple image patch, but instead is a flexible image deformation function. This enables the creation of more expressive dictionaries, capable of generalizing to a far greater variety of image patterns, while using a far smaller amount of ground-truthed images for supervised dictionary training. Deformation similarity is employed for patch matching to find the best set of atoms in the difference image (DI) dictionary for reconstructing image patches for a new input DI. Each such atom can be deformed to achieve a better match, thus extending generality while reducing the number of atoms needed in the dictionary. Multiple deformed atoms are weighted and combined to best reconstruct the input DI patch. Then, the same set of deformations and weights is projected to the corresponding atoms in the CD dictionary to obtain the output change-detection map. Experiments in six realistic synthetic aperture radar data sets demonstrate the robustness and efficiency of the proposed method in comparison with five other state-of-the-art methods from the literature.

Hyperspectral image (HSI) noise reduction is an active research topic in HSI processing due to its significance in improving the performance for object detection and classification. In this paper, we propose a joint spectral and spatial low-rank (LR) regularized method for HSI denoising, based on the assumption that the free-noise component in an observed signal can exist in latent low-dimensional structure while the noise component does not have this property. The proposed HSI denoising method not only considers the traditional LR property across the spectral domain but also leverages nonlocal LR property over the spatial domain. The main contribution of this paper is the incorporation of the low-rankness-based nonlocal similarity into sparse representation to characterize the spatial structure. Specially, the similar patches in each cluster usually contain similar sharp structure such as edges and textures; LR performed on cluster entitles to achieve a lower rank than that on the global spectral correlation. To make the proposed method more tractable and robust, we develop a variable splitting-based technique to solve the optimization problem. Experiment results on both simulated and real hyperspectral data sets demonstrate that the proposed method outperforms state-of-the-art methods with significant improvements both visually and quantitatively.

This paper presents an interactive feedback scheme of spatial resolution enhancement and spectral unmixing in hyperspectral imaging. Traditionally spatial resolution enhancement and spectral unmixing operations have been carried out separately, often in series. In such sequential processing, spatially enhanced hyperspectral images (HSIs) may introduce distortion in spectral fidelity making spectral unmixing results unreliable, or vice versa. Since both high- and low-resolution HSIs have the same endmembers, the deviation in spectral unmixing between targets and estimated high-resolution HSIs can be used as feedback to control spatial resolution enhancement. The spatial difference before and after unmixing can also be used as feedback to enhance spectral unmixing. Therefore, spectral unmixing is utilized as a constraint to spatial resolution enhancement, while spatial resolution enhancement helps improve spectral unmixing results. The performance of spatial resolution enhancement and spectral unmixing can be improved since one behaves like a prior to the other. Experimental results on both simulated and real HSI data sets demonstrate that the proposed interactive feedback scheme simultaneously achieved spatial resolution enhancement and spectral unmixing fidelity. This paper is an extended version of the previous work.

Hyperspectral image (HSI) denoising is significant for correct interpretation. In this paper, a sparse representation framework that unifies denoising and spectral unmixing in a closed-loop manner is proposed. While conventional approaches treat denoising and unmixing separately, the proposed scheme utilizes spectral information from unmixing as feedback to correct spectral distortion. Both denoising and spectral unmixing act as constraints to the others and are solved iteratively. Noise is suppressed via sparse coding, and fractional abundance in spectral unmixing is estimated using the sparsity prior of endmembers from a spectral library. The abundance of endmembers is used as a spectral regularizer for denoising based on the hypothesis that spectral signatures obtained from a denoising process result are close to those of unmixing. Unmixing restrains spectral distortion and results in better denoising, which reciprocally leads to further improvements in unmixing. The strength of our proposed method is illustrated by simulated and real HSIs with performance competitive to the state-of-the-art denoising and unmixing methods.

This paper presents a technique that removes specular reflection in an image without sacrificing texture and chromaticity fidelity in diffuse regions of the image. This method is based on the observation that there exist structural similarity both in diffuse and specular components with original highlight image as well as strong local correlation in chromaticity in diffuse component. Local correlation in chromaticity regularizes chromaticity consistency. Structural similarity is measured using a gradient magnitude similarity map of a diffuse region. A diffuse component is estimated by solving local structure and chromaticity joint compensation problem. Experiment results show that the proposed method outperforms state-of-the-art specular reflection removal methods in terms of visual evaluation and the degree of information loss.

This paper presents estimation of head pose angles from a single 2D face image using a 3D face model morphed from a reference face model. A reference model refers to a 3D face of a person of the same ethnicity and gender as the query subject. The proposed scheme minimizes the disparity between the two sets of prominent facial features on the query face image and the corresponding points on the 3D face model to estimate the head pose angles. The 3D face model used is morphed from a reference model to be more specific to the query face in terms of the depth error at the feature points. The morphing process produces a 3D face model more specific to the query image when multiple 2D face images of the query subject are available for training. The proposed morphing process is computationally efficient since the depth of a 3D face model is adjusted by a scalar depth parameter at feature points. Optimal depth parameters are found by minimizing the disparity between the 2D features of the query face image and the corresponding features on the morphed 3D model projected onto 2D space. The proposed head pose estimation technique was evaluated on two benchmarking databases: 1) the USF Human-ID database for depth estimation and 2) the Pointing'04 database for head pose estimation. Experiment results demonstrate that head pose estimation errors in nodding and shaking angles are as low as 7.93° and 4.65° on average for a single 2D input face image.

Nowadays mobile phones have been widely used, and Android is one of the most popular mobile operating system. The security issue of Android has caught great concerns among mobile users and researchers. In this paper, we study the vulnerabilities related of phone cameras. Specifically, we discover and present several camera-based attacks including the basic camera attack and advanced passcode inference attacks. We implement these attacks on real phones (with anti-virus software installed) and demonstrate the feasibility and effectiveness of the attacks. Furthermore, a lightweight defense scheme is proposed to secure phones against these attacks.

In digital forensics, recovery of a damaged or altered video file plays a crucial role in searching for evidences to resolve a criminal case. This paper presents a frame-based recovery technique of a corrupted video file using the specifications of a codec used to encode the video data. A video frame is the minimum meaningful unit of video data. Many existing approaches attempt to recover a video file using file structure rather than frame structure. In case a target video file is severely fragmented or even has a portion of video overwritten by other video content, however, video file recovery of existing approaches may fail. The proposed approach addresses how to extract video frames from a portion of video to be restored as well as how to connect extracted video frames together according to the codec specifications. Experiment results show that the proposed technique successfully restores fragmented video files regardless of the amount of fragmentations. For a corrupted video file containing overwritten segments, the proposed technique can recover most of the video content in non-overwritten segments of the video file.

This paper presents visual analysis of eye state and head pose (HP) for continuous monitoring of alertness of a vehicle driver. Most existing approaches to visual detection of nonalert driving patterns rely either on eye closure or head nodding angles to determine the driver drowsiness or distraction level. The proposed scheme uses visual features such as eye index (EI), pupil activity (PA), and HP to extract critical information on nonalertness of a vehicle driver. EI determines if the eye is open, half closed, or closed from the ratio of pupil height and eye height. PA measures the rate of deviation of the pupil center from the eye center over a time period. HP finds the amount of the driver's head movements by counting the number of video segments that involve a large deviation of three Euler angles of HP, i.e., nodding, shaking, and tilting, from its normal driving position. HP provides useful information on the lack of attention, particularly when the driver's eyes are not visible due to occlusion caused by large head movements. A support vector machine (SVM) classifies a sequence of video segments into alert or nonalert driving events. Experimental results show that the proposed scheme offers high classification accuracy with acceptably low errors and false alarms for people of various ethnicity and gender in real road driving conditions.

This paper proposes a band-subset-based clustering and fusion technique to improve the classification performance in hyperspectral imagery. The proposed method can account for the varying data qualities and discrimination capabilities across spectral bands, and utilize the spectral and spatial information simultaneously. First, the hyperspectral data cube is partitioned into several nearly uncorrelated subsets, and an eigenvalue-based approach is proposed to evaluate the confidence of each subset. Then, a nonparametric technique is used to extract the arbitrarily-shaped clusters in spatial-spectral domain. Each cluster offers a reference spectral, based on which a pseudosupervised hyperspectral classification scheme is developed by using evidence theory to fuse the information provided by each subset. The experimental results on real Hyperspectral Digital Imagery Collection Experiment (HYDICE) demonstrate that the proposed pseudosupervised classification scheme can achieve higher accuracy than the spatially constrained fuzzy c-means clustering method. It can achieve nearly the same accuracy as the supervised K-Nearest Neighbor (KNN) classifier but is more robust to noise.

This paper presents a spectral band selection method for feature dimensionality reduction in hyperspectral image analysis for detecting skin tumors on poultry carcasses. A hyperspectral image contains spatial information measured as a sequence of individual wavelength across broad spectral bands. Despite the useful information for skin tumor detection, real-time processing of hyperspectral images is often a challenging task due to the large amount of data. Band selection finds a subset of significant spectral bands in terms of information content for dimensionality reduction. This paper presents a band selection method of hyperspectral images based on the recursive divergence for the automatic detection of poultry carcasses. For this, we derive a set of recursive equations for the fast calculation of divergence with an additional band to overcome the computational restrictions in real-time processing. A support vector machine is used as a classifier for tumor detection. From our experiments, the proposed band selection method shows high detection accuracy with low false positive rates compared to the canonical analysis at a small number of spectral bands. Also, compared with the enumeration approach of 93.75% detection rate, our proposed recursive divergence approach gives 90.6% detection rate, which is within the industry-accepted accuracy of 90–95%, while achieving the computational saving for real-time processing.

This paper presents evolvable block-based neural networks (BbNNs) for personalized ECG heartbeat pattern classification. A BbNN consists of a 2-D array of modular component NNs with flexible structures and internal configurations that can be implemented using reconfigurable digital hardware such as field-programmable gate arrays (FPGAs). Signal flow between the blocks determines the internal configuration of a block as well as the overall structure of the BbNN. Network structure and the weights are optimized using local gradient-based search and evolutionary operators with the rates changing adaptively according to their effectiveness in the previous evolution period. Such adaptive operator rate update scheme ensures higher fitness on average compared to predetermined fixed operator rates. The Hermite transform coefficients and the time interval between two neighboring R-peaks of ECG signals are used as inputs to the BbNN. A BbNN optimized with the proposed evolutionary algorithm (EA) makes a personalized heartbeat pattern classifier that copes with changing operating environments caused by individual difference and time-varying characteristics of ECG signals. Simulation results using the Massachusetts Institute of Technology/Beth Israel Hospital (MIT-BIH) arrhythmia database demonstrate high average detection accuracies of ventricular ectopic beats (98.1%) and supraventricular ectopic beats (96.6%) patterns for heartbeat monitoring, being a significant improvement over previously reported electrocardiogram (ECG) classification results.

This paper presents a hardware implementation approach for Block-based Neural Networks (BbNNs) on a Programmable System-On-Chip. This is an intrinsic online evolution system that can be genetically evolved and adapted to changes in input data patterns dynamically without any need for multiple FPGA reconfigurations to accommodate various network structure/parameter changes. This removes a considerable bottleneck for performance. The research presented here is a first step towards an evolvable system that can be implemented as an embedded system.

This paper presents continuous heartbeat monitoring using evolvable block-based neural networks (BbNNs). An evolutionary algorithm is used to optimize the structure and weights of BbNN simultaneously. A BbNN, trained with the Hermite transform coefficients and a time interval between the two neighboring R peaks of ECG signal, promises a patient-specific heartbeat monitoring system. BbNNs reconfigure the structure and internal weights to cope with individual differences and the changes in physical conditions. Simulation results using the MIT-BIH Arrhythmia database demonstrate a high accuracy of 98.7% on average for the classification of ventricular ectopic beats (VEBs), being a substantial improvement over conventional techniques.

This paper investigates the application of evolvable block-based neural networks (BbNNs) to ECG signal classification. A BbNN consists of a two-dimensional (2-D) array of modular basic blocks that can be easily implemented using reconfigurable digital hardware. BbNNs are evolved for each patient in order to provide personalized health monitoring. A genetic algorithm evolves the internal structure and associated weights of a BbNN using training patterns that consist of morphological and temporal features extracted from the ECG signal of a patient. The remaining part of the ECG record serves as the test signal. The BbNN was tested for ten records collected from different patients provided by the MIT-BIH Arrhythmia database. The evolved BbNNs produced higher than 90% classification accuracies.

This paper presents evolvable neural networks based on a developmental model for navigation control of autonomous mobile robots in dynamic operating environments. Bio-inspired mechanisms have been applied to autonomous design of artificial neural networks for solving practical problems. The proposed neural network architecture is grown from an initial developmental model by a set of production rules of the L-system that are represented by the DNA coding. The L-system is based on parallel rewriting mechanism motivated by the growth models of plants. DNA coding gives an effective method of expressing general production rules. Experiments show that the evolvable neural network designed by the production rules of the L-system develops into a controller for mobile robot navigation to avoid collisions with the obstacles.

This paper presents a time series prediction technique using the block-based neural networks (BbNNs). Building a model dynamical system can be a general approach to the time series prediction problem. However, the functional form and the order of the dynamics of the process generating the time series data are usually unknown. BbNNs, an evolvable neural network model with simultaneous optimization of network structure and connection weights by use of evolutionary algorithms, provide a model-free estimation of underlying nonlinear dynamical systems. Empirical results with a benchmark Mackey-Glass time series show that the evolved BbNNs can predict the future behavior of a complex dynamical system with sufficient accuracy.

This paper presents detection of skin tumor on poultry carcasses using hyperspectral fluorescence images. Image samples are obtained from a hyperspectral imaging system that provides digital images of 65 spectral bands with wavelength ranging from 425 [nm] to 711 [nm]. The principal component analysis (PCA) technique finds an effective representation of spectral signature in a reduced dimensional feature space. A support vector machine (SVM) classifies the feature vectors and makes a decision whether each pixel falls in normal or tumor categories.