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Image inpainting based on sparse representation using self-similar joint sparse coding

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第13期82卷 20215-20231页

作者： Zhang, Lei Chang, Minhui Chen, Rui Yuncheng Univ Sch Math & Informat Technol Yuncheng 044000 Peoples R China Zhengzhou Univ Light Ind Software Engn Coll Zhengzhou 450001 Peoples R China

In order to improve the sparse coding ability of over-complete dictionary and take advantage of the similarity between damaged pixels and their neighbors, we propose an inpainting method based on sparse representation using self-similar joint sparse coding. First, we perform singular value decomposition on the gradient vector of the image patches, and then divide the image patches into three categories: smooth patches, edge patches and texture patches according to the relationship between the primary direction and the secondary direction. Second, we use the KSVD method to train these three types of image patches respectively, and obtain three over-complete dictionaries that adapt to different local features. Third, we define a non-local self-similar matching function and use it to search for the most similar image patch to the current patch in the target region, and then use the similar patch and the current patch for joint sparse coding. Finally, we use the calculated sparse coding and the corresponding over-complete dictionary to reconstruct the current patch. A series of experimental results show that the self-similar joint sparse coding we proposed can not only improve the restoration effect of sparse representation methods to a certain extent, but also has good adaptability and can be combined with other sparse representation methods to improve their restoration effect.

关键词： Image inpainting sparse representation Self-similar joint sparse coding

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Depth image super-resolution based on joint sparse coding

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PATTERN RECOGNITION LETTERS 2020年 130卷 21-29页

作者： Li, Beichen Zhou, Yuan Zhang, Yeda Wang, Aihua Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

This paper proposes a new approach to single depth image super-resolution (SR), based upon a novel joint sparse coding model. A low-resolution color is used as a guide in the SR process. Firstly, we introduce synthetic characteristic image patch to learn a joint dictionary from the low-resolution depth map as well as its corresponding low-resolution intensity image. Then, we derive the joint nonlocal center sparse representation model based on sparse coding and theoretical analysis. In reconstruction process, we use Bayesian interpretation approach to estimation the sparse code coefficients for each unknown HR image patch. Meanwhile, we use an iterative algorithm to solve the JSC model. In addition, we exploit image patch redundancy within and across different scales, produce visually pleasing results without extensive training on external database. Experimental results demonstrate that the proposed method outperforms favorably many current state-of-the-art depth map super-resolution approaches on both visual effects and objective image quality and underpin the validity of our proposed model. Published by Elsevier B.V.

关键词： Image super-resolution joint sparse coding

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MULTI-VIEW joint sparse coding FOR IMAGE ANNOTATION

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2017年第4期13卷 1407-1414页

作者： Zang, Miao Xu, Huimin North China Univ Technol Sch Elect & Informat Engn 5 Jinyuanzhang Rd Beijing 100144 Peoples R China Beijing Univ Posts & Telecommun Sch Informat & Commun Engn 10 Xitucheng Rd Beijing 100876 Peoples R China

Automatic image annotation has become an increasingly important research topic since it is the critical and challenging component for image retrieval and management. In order to exploit the complementary information of different types of image features as well as the semantic information concurrently, we present a multi-view joint sparse coding (MVJSC) framework for image annotation, in which handcrafted features as well as deep learning based feature and label information are treated as distinct views and are exploited adaptively by multi-view learning. The adopted sparse coefficient matrix for each view is different since different view contributes differently to the final decision, and a joint sparse regularization term is introduced to ensure the similar sparse pattern across multiple views. Using the learned sparse coefficients as well as the dictionary for each view from the training images, a novel label transfer scheme is also proposed. Experiments on Corel 5K and ESP Game datasets have evaluated an improved performance of the proposed method compared with other related state-of-the-art studies.

关键词： Multi-view learning Image annotation joint sparse coding Dictionary learning Deep learning

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Sonar-based Place Recognition using joint sparse coding Method

Sonar-based Place Recognition using Joint Sparse Coding Meth...

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International joint Conference on Neural Networks (IJCNN)

作者： Zheng, Xiangmei Liu, Huaping Sun, Fuchun Gao, Meng Li, Jiakui Zhang, Qing Shijiazhuang Tiedao Univ Dept Elect & Elect Engn Shijiazhuang Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Tsinghua Univ TNLIST State Key Lab Intelligent Technol & Syst Beijing Peoples R China Samsung Res Inst China Beijing Peoples R China

ISBN: (纸本)9781509006199

The problem of place recognition is central to robot navigation. The robot needs to be able to recognize or at least to be able to estimate the likelihood that it has been at a place before when it has returned to a previously visited place. We cast the place recognition problem as one of classifying among multiple linear regression models, and argue that new theory from sparse signal representation offers the key to addressing the problem. In this paper, a joint kernel sparse coding model is developed to tackle the multivariate sonar samples place recognition problem. The experimental results show that the joint sparse coding achieves better performance than 1-Nearest Neighborhood (1-NN) method.

关键词： joint sparse coding place recognition sonar kernel sparse coding

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joint sparse coding based spatial pyramid matching for classification of color medical image

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COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 2015年 41卷 61-66页

作者： Shi, Jun Li, Yi Zhu, Jie Sun, Haojie Cai, Yin Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Nantong Univ Coll Med Dept Histol & Embryol Nantong Peoples R China

Although color medical images are important in clinical practice, they are usually converted to grayscale for further processing in pattern recognition, resulting in loss of rich color information. The sparse coding based linear spatial pyramid matching (ScSPM) and its variants are popular for grayscale image classification, but cannot extract color information. In this paper, we propose a joint sparse coding based SPM (JScSPM) method for the classification of color medical images. A joint dictionary can represent both the color information in each color channel and the correlation between channels. Consequently, the joint sparse codes calculated from a joint dictionary can carry color information, and therefore this method can easily transform a feature descriptor originally designed for grayscale images to a color descriptor. A color hepatocellular carcinoma histological image dataset was used to evaluate the performance of the proposed JScSPM algorithm. Experimental results show that JScSPM provides significant improvements as compared with the majority voting based ScSPM and the original ScSPM for color medical image classification. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： joint sparse coding Spatial pyramid matching Color medical image Classification

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Tactile Sequence Classification using joint Kernel sparse coding

Tactile Sequence Classification using Joint Kernel Sparse Co...

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International joint Conference on Neural Networks (IJCNN)

作者： Yang, Jingwei Liu, Huaping Sun, Fuchun Gao, Meng Shijiazhuang Tiedao Univ Dept Elect & Elect Engn Shijiazhuang Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ TNLIST State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China

ISBN: (纸本)9781479919598

Tactile sensors in the robotic fingertips are used to capture multiple object properties such as texture, roughness, spatial features, compliance or friction and therefore becomes a very important sense modality for intelligent robot. However, existing work neglects the intrinsic relation between different fingers which simultaneously contact the object. In this paper, a joint kernel sparse coding model is developed to tackle the multi-finger tactile sequence classification problem. In this model, the intrinsic relations between fingers are explicitly considered using the joint sparse coding which encourages different modal coding to share the same support. The experimental results show that the joint sparse coding achieves better performance than conventional sparse coding.

关键词： joint sparse coding kernel sparse coding tactile sequence classification

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Tactile Sequence Classification using joint Kernel sparse coding

Tactile Sequence Classification using Joint Kernel Sparse Co...

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International joint Conference on Neural Networks

作者： Jingwei Yang Huaping Liu Fuchun Sun Meng Gao Department of Electrical and Electronic Engineering Shijiazhuang Tiedao University Shijiazhuang China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9781479919611

关键词： joint sparse coding kernel sparse coding tactile sequence classification sparse coding tactile sensors Fingers joints joints Touch Seeds friction

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Sparsity-Based Clustering for Large Hyperspectral Remote Sensing Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2021年第12期59卷 10410-10424页

作者： Zhai, Han Zhang, Hongyan Zhang, Liangpei Li, Pingxiang China Univ Geosci Sch Geog & Informat Engn Wuhan 430074 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China

Hyperspectral image (HSI) clustering is extremely challenging because of the complexity of the image structure. Recently, the subspace clustering algorithms have achieved competitive performance for HSIs. However, these methods generally are computationally complex and time-and-memory-consuming, given their reliance on large-scale adjacency matrix learning and graph segmentation, which limits their application to large HSIs and reduces their attractiveness in real applications. In this article, in view of this, two novel sparsity-based clustering algorithms are proposed for large HSIs, named sparse coding-based clustering (SCC) and joint SCC (JSCC). To the best of our knowledge, we are the first to use the sparse representation recovery residual to cluster HSIs. Based on a structured dictionary constructed by -nearest neighbor (KNN), an SCC model is constructed to cluster HSIs according to the recovery residual minimization criterion. By dealing with a pixel-wise sparse recovery problem instead of the large-scale graph optimization problem of the whole image, the computational complexity and the time-and-memory cost are reduced to a large degree, which makes sense for practical applications. Then, by introducing the super-pixel neighborhood, a JSCC model is constructed to better explore the interpixel correlation of HSIs and further improve the clustering performance. The proposed algorithms were verified on three widely used HSIs. All the three experiments confirm the effectiveness of the proposed algorithms, which can be considered as competitive tools for use with large HSIs.

关键词： Dictionaries Computational modeling Biological system modeling Hyperspectral imaging Encoding Clustering algorithms Clustering methods Clustering hyperspectral image (HSI) joint sparse coding recovery residual sparse coding

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joint coupled dictionaries-based visible-infrared image fusion method via texture preservation structure in sparse domain

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COMPUTER VISION AND IMAGE UNDERSTANDING 2023年第1期235卷

作者： Zhang, Chengfang Li, Haoyue Feng, Ziliang He, Sidi Sichuan Univ Natl Key Lab Fundamental Sci Synthet Vis 24Sect 1South 1st ring Rd Chengdu 610039 Peoples R China Sichuan Police Coll Intelligent Policing Key Lab Sichuan Prov 186 longtouguan Rd Luzhou 646000 Peoples R China Sichuan Univ Coll Comp Sci 24Sect 1South 1st ring Rd Chengdu 610039 Peoples R China

joint sparse coding (JSC) and coupled dictionary learning (CDL) have been successful in visible and infrared image fusion tasks. However, JSC fusion methods using a single dictionary cannot perfectly characterize different modal signals. The CDL-based fusion methods ignore the role of high-frequency components in texture preservation and sharpness. In this study, we design fusion rules while considering the uniqueness and connection of different feature spaces to alleviate this issue. In the proposed fusion method, coupled dictionaries are first used to establish the relationship between infrared and visible images. Then, we use joint sparse coding and coupled dictionaries to calculate sparse coefficients via the joint sparse model. Finally, the improved coefficient fusion strategy realizes the conversion and synthesis between different spatial images. Experimental results on the TNO and RoadScene datasets show that the proposed method achieves promising results in terms of objective and subjective performance compared with state-of-the-art algorithms.

关键词： Visible and infrared image fusion Coupled dictionary learning joint sparse coding Texture preserving

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Visual-Tactile Fusion for Object Recognition

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2017年第2期14卷 996-1008页

作者： Liu, Huaping Yu, Yuanlong Sun, Fuchun Gu, Jason Tsinghua Univ State Key Lab Intelligent Technol & Syst Dept Comp Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Tsinghua Natl Lab Informat Sci & Technol Dept Comp Sci & Technol Beijing 100084 Peoples R China Fuzhou Univ Coll Comp Fuzhou 350108 Peoples R China Dalhousie Univ Dept Elect & Comp Engn Halifax NS B3H 4R2 Canada

The camera provides rich visual information regarding objects and becomes one of the most mainstream sensors in the automation community. However, it is often difficult to be applicable when the objects are not visually distinguished. On the other hand, tactile sensors can be used to capture multiple object properties, such as textures, roughness, spatial features, compliance, and friction, and therefore provide another important modality for the perception. Nevertheless, effective combination of the visual and tactile modalities is still a challenging problem. In this paper, we develop a visualtactile fusion framework for object recognition tasks. This paper uses the multivariate-time-series model to represent the tactile sequence and the covariance descriptor to characterize the image. Further, we design a joint group kernel sparse coding (JGKSC) method to tackle the intrinsically weak pairing problem in visual-tactile data samples. Finally, we develop a visual-tactile data set, composed of 18 household objects for validation. The experimental results show that considering both visual and tactile inputs is beneficial and the proposed method indeed provides an effective strategy for fusion. Note to Practitioners-Visual and tactile measurements offer complementary properties that make them particularly suitable for fusion in order to address the robust and accurate recognition of objects, which is a necessity for many automation systems. In this paper, we investigate a widely applicable scenario in grasp manipulation. When identifying an object, the manipulator may see it using the camera and touch it using its hand. Thus, we obtain a pair of test samples, including one image sample and one tactile sample. The manipulator then utilizes this sample pair to identify this object with a classifier that is constructed using the previously collected training samples. However, when collecting training samples, we may collect the image samples and the tactile samples separa

关键词： joint sparse coding object recognition tactile perception visual perception

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