Convolutional Sparse Representation Research Articles

Nursing Analysis Based on Medical Imaging Technology before and after Coronary Angiography in Cardiovascular Medicine.

With the advancement of technology, medical imaging technology has been greatly improved. This article mainly studies the nursing before and after coronary angiography in cardiovascular medicine based on medical imaging technology. This paper proposes a multimodal medical image fusion algorithm based on multiscale decomposition and convolution sparse representation. The algorithm first decomposes the preregistered source medical image by NSST, takes the subimages of different scales as training images, and optimizes the subdictionaries of different scales; then convolution and sparse the subimages on each scale encoding to obtain the sparse coefficients of different subimages; secondly, the combination of improved L1 norm and improved spatial frequency (novel sum-modified SF (NMSF)) is used for high-frequency subimage coefficients, and the fusion of low-frequency subimages improved the rule of combining the L1 norm and the regional energy; finally, the final fused image is obtained by inverse NSST of the fused low-frequency subband and high-frequency subband. Experimental analysis found that the bifurcation angle has nothing to do with the damage of the branch vessels after the main branch stent is placed. The bifurcation angle greater than 50° is an independent predictor of MACE after stent extrusion for bifurcation lesions. Experimental results show that the proposed method has good performance in contrast enhancement, detail extraction, and information retention, and it improves the quality of the fusion image.

Supervised anomaly detection by convolutional sparse representation

Supervised anomaly detection by convolutional sparse representation

A Competent Convolutional Sparse Representation Model for Pan-Sharpening of Multi-Spectral Images

Two types of images are produced by Earth observation satellites, each having complementing spatial andspectral characteristics. Pan-sharpening (PS) is based on remote sensing and image fusion approach thatproduces a high spatial resolution multi-spectral image by merging spectral information from a low spatialresolution multispectral (MS) image with intrinsic spatial details from a high spatial resolution panchromatic(PAN) image. Traditional pan-sharpening methods continue to seek for a fused image that contains thenecessary spatial and spectral information. This work proposes a pan-sharpening method based on a recentinvention, convolutional sparse representation (CSR). Geometric structural characteristics are extracted fromthe PAN image using a CSR-based filtering procedure. The challenge of learning filters, convolutional basispursuit denoising (CBPDN), is handled using a modified dictionary learning method based on the concept ofAlternating Direction Method of Multipliers (ADMM). The retrieved details are put into MS bands usingapplicable weighting coefficients. Because the proposed fusion model avoids the standard patch-basedmethod, spatial and structural features are preserved while spectral quality is maintained. The spectraldistortion index SAM and the spatial measure ERGAS improve by 4.4 and 6.2 percent, respectively, whencompared to SR-based techniques. The computational complexity is reduced by 200 seconds when compared
 to the most recent SR-based fusion technique. The proposed method's efficacy is demonstrated by reduced-scale and full-scale experimental findings utilising the QuickBird and GeoEye-1 datasets.

Medical Image Fusion Using Deep Learning Mechanism

Abstract: Sparse representation(SR) model named convolutional sparsity based morphological component analysis is introduced for pixel-level medical image fusion. The CS-MCA model can achieve multicomponent and global SRs of source images, by integrating MCA and convolutional sparse representation(CSR) into a unified optimization framework. In the existing method, the CSRs of its gradient and texture components are obtained by the CSMCA model using pre-learned dictionaries. Then for each image component, sparse coefficients of all the source images are merged and then fused component is reconstructed using the corresponding dictionary. In the extension mechanism, we are using deep learning based pyramid decomposition. Now a days deep learning is a very demanding technology. Deep learning is used for image classification, object detection, image segmentation, image restoration. Keywords: CNN, CT, MRI, MCA, CS-MCA.

Convolutional analysis operator learning for multifocus image fusion

Sparse representation (SR), convolutional sparse representation (CSR) and convolutional dictionary learning (CDL) are synthetic-based priors that have proven to be successful in signal inverse problems (such as multifocus image fusion). Unlike “synthesis” formulas, “analysis” model assigns probabilities to signals through various forward measurements of signals. Analysis operator learning (AOL) is a classical analysis-based learning method. Convolutional analysis operator learning (CAOL) is convolutional form of AOL. CAOL uses unsupervised learning method to train autocoded convolutional neural network (CNN) to more accurately solve inverse problem. From the perspective of CAOL, this paper introduces learned convolutional regularizers into multifocus image fusion and proposes CAOL-based multifocus image fusion algorithm. In the CDL stage, convergent block proximal extrapolated gradient method with majorizer (BPEG-M) and adaptive momentum restarting scheme are used. In the sparse fusion stage, alternating direction method of multipliers (ADMM) approach with convolutional basis pursuit denoising (CBPDN) and l1 norm maximum strategy are employed for high-frequency and low-frequency component, respectively. 3 types of multifocus images (static gray images, gray images in sports and color images) are tested to verify performance of the proposed method. A comparison with representative methods demonstrates superiority of our method in terms of subjective observation and objective evaluation.

Fusion of Hyperspectral and Multispectral Images by Convolutional Sparse Representation

Sparse representation (SR)-based methods have achieved numerous successes in the fusion of hyperspectral and multispectral images (HSIs and MSIs). However, in many SR-based fusion methods, due to patch dividing, it is hard to make pixel values across boundaries of contiguous patches be exactly consistent, which limits the ability to preserve scene details. To remedy such deficiency, a fusion framework is proposed for HSIs and MSIs by using convolutional sparse representation (FCS). This novel fusion method consists of three stages: 1) the spectral dictionary is trained by the convolutional sparse dictionary learning algorithm to extract spectral information from HSIs; 2) hyperspectral and multispectral transferring matrices are estimated to map HSIs and MSIs onto the space of high-resolution hyperspectral images (HR-HSIs); and 3) we construct the convolutional sparse fusion model for HR-HSIs. Different from those traditional patch-based SR fusion methods, the FCS method focuses on the whole images instead of dividing patches, which can suppress the limitation of scene detail preservation caused by sparse coding on independent patches. Also, it belongs to a kind of online learning without lots of training samples. The Pavia dataset and the Paris dataset are used to evaluate the performance of our method. Experimental results indicate that the FCS method achieves much fusion performance compared with commonly used and state-of-the-art algorithms.

Infrared and Visible Image Fusion Method Based on Rolling Guidance Filter and Convolution Sparse Representation

Infrared and Visible Image Fusion Method Based on Rolling Guidance Filter and Convolution Sparse Representation

A multi‐focus image fusion method based on multi‐source joint layering and convolutional sparse representation

In this paper, a new Multi-Focus Image Fusion (MFIF) method based on multi-source joint layering and Convolutional Sparse Representation (CSR) is proposed. Based on the characteristics of multi-focus source images, a multi-source joint layering regularization model was designed to divide the sources into a common base-layer and respective focus detail-layers. This strategy can overcome the defects caused by source layering separately effectively. In detail-layer fusion, CSR was employed to extract and global features. It can avoid detail blur and high computational cost caused by image blocking in the conventional sparse representation model. The proposed detail-layer fusion rule combined the CSR coefficient maps pairwise with the window based select-max rule. In the experiments, the optimal layering parameter was selected by experiments at first, and then five recently proposed specific MFIF or general image fusion algorithms were contrasted with the proposed method by plenty of subjective and objective experimental comparisons. The experimental results demonstrated the superiority of the authors’ method.

Multifocus image fusion using convolutional dictionary learning with adaptive contrast enhancement

Multifocus image fusion technology uses a mathematical model to integrate multifocus areas to obtain full-focus, clear images. The fusion method based on convolutional sparse representation (CSR) trains and “learns” translation-invariant filters, thereby addressing the missing signal infrastructure and high redundancy of the patch-based method. Convolutional dictionary learning and CSR rely on the alternating-direction method of multipliers and ignore model matching between the training and testing phases, leading to convergence difficulties due to the tricky parameter tuning. The block proximal extrapolated gradient method using the majorization and gradient-based restarting scheme (reG-BPEG-M) adopts the driving force coefficient formula and adaptive restart rule to solve the model mismatch problem. We introduce reG-BPEG-M into multifocus image fusion to update filters and sparse code using two-block and multiblock schemes. Compared with other state-of-the-art fusion methods, our strategy reduces model mismatch and improves the convergence of fusion for gray and color multifocus images.

Multimodal medical image fusion based on nonsubsampled shearlet transform and convolutional sparse representation

Multimodal medical image fusion based on nonsubsampled shearlet transform and convolutional sparse representation

Adversarial network integrating dual attention and sparse representation for semi-supervised semantic segmentation

Semantic segmentation is the important task of assigning a semantic label to each pixel. However, semantic segmentation based on the deep neural network usually requires massive annotations consumption to acquire better performance. To avoid the problem, some algorithms based on weakly-supervised and semi-supervised conditions have been proposed and achieved gradually improving performance in recent years. In this paper, we propose a novel semi-supervised adversarial network to alleviate the shortage of labeled data, which only requires a few labeled images to get competitive performance. The model is composed of two parts: the segmentation network and the discriminator network. The first part aims to semantically generate a segmented result that has the same size as the input color image. The discriminator network is designed in a fully convolutional manner to distinguish the predicted probability maps depending on the ground truth distribution. In particular, the probability maps are regarded as focal attention maps, which are fed back to the segmentation network to make the model converge faster, and the process can induce the model to focus on pixels that are hard to segment. To enhance the representation ability of image features, sparse representation and dual attention are adopted in the segmentation network. The sparse representation module aims to emphasize the object edges and locations by learning the convolutional sparse representation of the input color images, and the dual attention module can exploit the semantic interdependencies in two different dimensions. Moreover, the semi-supervised mechanism is introduced to the network, in which the adaptive parameter T that controls the sensitivity of the self-taught phase is proposed, and the training dataset is split into two parts for fully-supervised learning and semi-supervised learning. Specifically, the first part is unlabeled data, which is applied to provide supervised signals for semi-supervised training. The labeled data drawn from the other part is utilized for fully-supervised learning. Our semi-supervised adversarial framework can improve the learning ability and achieve higher performance, also, provide a novel approach to tackle the semantic segmentation task. Finally, comprehensive experiments on the PASCAL VOC 2012 and Cityscapes datasets are conducted to verify the effectiveness of the proposed model, which achieves the expected performance.

Infrared and visible image fusion based on convolutional sparse representation and guided filtering

Infrared and visible image fusion is a hot research direction in the fields of computer vision and image processing, and it is a common multimodal image fusion. An effective image fusion algorithm via convolutional sparse representation (CSR) and guided filtering is proposed for fusing infrared and visible images in this paper. First, a series of dictionary filters are trained by the CSR strategy, and the smooth image component and detailed image component are obtained by classifying those filters into high-pass filters and low-pass filters. Then two rules are designed to fuse the smooth image component and detailed image component, respectively. For the detailed image component, a weight construction method based on guided filtering is designed to get the weight maps, and the smooth image component is fused by applying the “choose-max” strategy to the corresponding sparse coefficients. Finally, the fused image is obtained by combining the fused smooth image component and detailed image component. Experimental results show that the proposed algorithm achieves good fusion results and exhibits advantages over comparison image fusion methods.

Morphological Component Analysis-Based Perceptual Medical Image Fusion Using Convolutional Sparsity-Motivated PCNN

This paper proposes a perceptual medical image fusion framework based on morphological component analysis combining convolutional sparsity and pulse-coupled neural network, which is called MCA-CS-PCNN for short. Source images are first decomposed into cartoon components and texture components by morphological component analysis, and a convolutional sparse representation of cartoon layers and texture layers is produced by prelearned dictionaries. Then, convolutional sparsity is used as a stimulus to motivate the PCNN for dealing with cartoon layers and texture layers. Finally, the medical fused image is computed via combining fused cartoon layers and texture layers. Experimental results verify that the MCA-CS-PCNN model is superior to the state-of-the-art fusion strategy.

Man-Made Target Detection of PolSAR Image Based on Local Convolution Sparse Representation

Man-made target detection of PolSAR image is an important part of the application of remote sensing data, and generally, the target occupies very few pixels of the image. Convolutional sparse representation (CSR) is proved able to effectively combine polarimetric and spatial information of a PolSAR image to achieve target detection. However, the acquisition of prior knowledge and the modeling of background are difficult. Moreover, the solution process is highly dependent on the alternating direction method of multipliers (ADMMs) algorithm, which introduces new parameters and has high computational complexity. To solve these problems, this letter proposes a man-made target detection method for PolSAR images based on local CSR (Local-CSR). In this method, a novel target detection framework to combine the polarimetric and spatial information based on CSR is proposed, the target dictionary is constructed by the Local-CSR to avoid the aforementioned problems of ADMM solution procedure and finally realize the man-made target detection of the PolSAR image. Two sets of fully polarimetric SAR data sets are used to demonstrate the performance and the experimental results prove the capacity and validity of the proposed method.

Intelligent Fusion of Infrared and Visible Image Data Based on Convolutional Sparse Representation and Improved Pulse-coupled Neural Network

Multi-source information can be obtained through the fusion of infrared images and visible light images, which have the characteristics of complementary information. However, the existing acquisition methods of fusion images have disadvantages such as blurred edges, low contrast, and loss of details. Based on convolution sparse representation and improved pulse-coupled neural network this paper proposes an image fusion algorithm that decompose the source images into high-frequency and low-frequency subbands by non-subsampled Shearlet Transform (NSST). Furthermore, the low-frequency subbands were fused by convolutional sparse representation (CSR), and the high-frequency subbands were fused by an improved pulse coupled neural network (IPCNN) algorithm, which can effectively solve the problem of difficulty in setting parameters of the traditional PCNN algorithm, improving the performance of sparse representation with details injection. The result reveals that the proposed method in this paper has more advantages than the existing mainstream fusion algorithms in terms of visual effects and objective indicators.

Infrared and visible image fusion via rolling guidance filter and convolutional sparse representation

Infrared (IR) and visible (VIS) image fusion technology combines the complementary information of the same scene from IR and VIS imaging sensors to generate a composite image, which is beneficial to post image-processing tasks. In order to achieve good fusion performance, a method by combining rolling guidance filter (RGF) and convolutional sparse representation (CSR) is proposed. In the proposed method, RGF is performed on every pre-registered IR and VIS source images to obtain their detail layers and base layer. Then, the detail layers are fused with a serious of weighted coefficients produced by joint bilateral filer (JBF). The base layer is decomposed into a sub-detail-layer and a sub-base-layer. CSR is applied to fuse the sub-detail-layer and averaging strategy is used to fuse the sub-base-layer. Finally, the fused image is reconstructed by adding the fused detail layer and base layer. Experimental results demonstrate the superiority of our proposed method both in subjective and objective assessment.

Joint Polarimetric-Adjacent Features Based on LCSR for PolSAR Image Classification

Image classification is a critical and important application in PolSAR image interpretation. Finding a feature extraction method, which can effectively describe the characteristics of the target, is an important basis for image classification. In addition to unique polarimetric features of PolSAR system, spatial adjacent features of image also need to be considered. So in this article, a joint polarimetric-adjacent features extraction method based on local convolution sparse representation is proposed for PolSAR image classification. Firstly, this article uses convolutional sparse representation to achieve the convolution of the image filters and the feature responses so as to achieve the effective combination of the polarimetric and adjacent information of the image. Meanwhile, construct and train the dictionary using local strategy in the original domain to avoid the high computational complexity and the confusion of different grounds caused by global dictionary. Finally, support vector machine (SVM) is used to combine the extracted features to achieve the classification. Three sets of full polarimetric data are used and the experiment results prove that the proposed method can effectively combine the polarimetric and adjacent information of data and have a good performance in PolSAR image classification.

Research on Infrared and Visible Image Fusion Based on Tetrolet Transform and Convolution Sparse Representation

Image fusion is a visual enhancement technique that combines source images from different sensors to produce a more robust and informative fused image for subsequent processing or decision making. Infrared and visible light images share complementary properties that enable the production of robust and informative fused images. This paper proposed an infrared and visible image fusion method that improved the tetrolet framework to improve infrared and visible image fusion quality. First, the source image is enhanced by bicubic interpolation. The improved tetrolet transform then decomposes the enhanced source image; the high-frequency components are fused by convolutional sparse representation theory and combined with corresponding rules, and the low-frequency components are fused by defining ISER descriptors. Finally, we use the inverse transform to reconstruct the fused image. Qualitative and quantitative experimental results on five groups of typical infrared and visible image datasets demonstrate the proposed method's effectiveness. The proposed method exhibits better performances on subjective vision and objective indexes compared with the other state-of-the-art methods.

Two-Branch Convolutional Sparse Representation for Stereo Matching

Supervised learning methods have been used to calculate the stereo matching cost in a lot of literature. These methods need to learn parameters from public datasets with ground truth disparity maps. Due to the heavy workload used to label the ground truth disparities, the available training data are limited, making it difficult to apply these supervised learning methods to practical applications. The two-branch convolutional sparse representation (TCSR) model is proposed in the paper. It learns the convolutional filter bank from stereo image pairs in an unsupervised manner, which reduces the redundancy of the convolution kernels. Based on the TCSR model, an unsupervised stereo matching cost (USMC), which does not rely on the truth ground disparity maps, is designed. A feasible iterative algorithm for the TCSR model is also given and its convergence is proven. Experimental results on four popular data sets and one monocular video clip show that the USMC has higher accuracy and good generalization performance.

Fusion of visible and infrared image via compressive sensing using convolutional sparse representation

An effective visible light and infrared image fusion method using a deep learning framework is designed to obtain a fused image which contains all the features from infrared and visible images. First, the source images are decomposed into low frequency and high frequency sub bands using wavelet transform. Then the low frequency is fused by maximum fusion rule. For the high frequency sub bands a deep learning network is used to find activity level measurements and then fused using the maximum fusion rule. For reconstruction, the optimized orthogonal matching pursuit algorithm and inverse wavelet transform are used.