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Joint group sparse coding and weighted nuclear norm for Poisson denoising

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2025年 470卷

作者： Zhu, Jianguang Gao, Wen Wei, Ying Hao, Binbin Shandong Univ Sci & Technol Coll Math & Syst Sci Qingdao 266590 Peoples R China China Univ Petr Coll Sci Qingdao 266580 Peoples R China

It is difficult to remove Poisson noise because of its multiplicative and signal-dependent nature. In this paper, a new Poisson denoising model based on nonlocal self-similarity is introduced. It combines the weighted nuclear norm and group sparse coding as a regularization term, and makes full use of the low rank and sparse properties of similar image patches. Numerically, incorporating singular value decomposition and the variable splitting method, an alternating minimization method with an adaptive parameter selection strategy is proposed to resolve the new denoising model. Extensive experiments indicate that the proposed model outperforms the existing state-of-the-art Poisson denoising methods.

关键词： Poisson image denoising Dictionary learning The weighted nuclear norm group sparse coding Principal component analysis

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Locality-aware group sparse coding on Grassmann manifolds for image set classification

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NEUROCOMPUTING 2020年第0期385卷 197-210页

作者： Wei, Dong Shen, Xiaobo Sun, Quansen Gao, Xizhan Yan, Wenzhu Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

Riemannian sparse coding methods are attracting increasing interest in many computer vision applications, relying on its non-Euclidean structure. One such recently successful task is image set classification by the aid of Grassmann Manifolds, where an image set can be seen as a point. However, due to irrelevant information and outliers, the probe set may be represented by misleading sets with large sparse coefficients. Meanwhile, it is difficult for a single subspace to cover changes within an image set and the hidden structure among samples is relaxed. In this paper, we propose a novel Grassmann Locality-Aware group sparse coding model (GLGSC) that attempts to preserve locality information and take advantage of the relationship among image sets to capture the inter and intra-set variations simultaneously. Since the contributions of different gallery subspaces to the probe subspace should vary in importance, we then introduce a novel representation adaption term. In addition, a kernelised version of GLGSC is proposed to handle non-linearity in data. To reveal the effectiveness of our algorithm over state-of-the-art, several classification tasks are conducted, including face recognition, object recognition and gesture recognition. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Grassmann manifolds group sparse coding Locality preserving Image set classification

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From group sparse coding to rank minimization: A novel denoising model for low-level image restoration

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SIGNAL PROCESSING 2020年 176卷 107655-107655页

作者： Li, Yunyi Gui, Guan Cheng, Xiefeng Nanjing Univ Posts & Telecommun Coll Elect & Opt Engn Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Microelect Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210023 Peoples R China

Recently, low-rank matrix recovery theory has been emerging as a significant progress for various image processing problems. Meanwhile, the group sparse coding (GSC) theory has led to great successes in image restoration (IR) problem with each group contains low-rank property. In this paper, we propose a novel low-rank minimization based denoising model for IR tasks under the perspective of GSC, an important connection between our denoising model and rank minimization problem has been put forward. To overcome the bias problem caused by convex nuclear norm minimization (NNM) for rank approximation, a more generalized and flexible rank relaxation function is employed, namely weighted nonconvex relaxation. Accordingly, an efficient iteratively-reweighted algorithm is proposed to handle the resulting minimization problem combing with the popular L-1/2 and L-2/3 thresholding operators. Finally, our proposed denoising model is applied to IR problems via an alternating direction method of multipliers (ADMM) strategy. Typical IR experiments on image compressive sensing (CS), inpainting, deblurring and impulsive noise removal demonstrate that our proposed method can achieve significantly higher PSNR/FSIM values than many relevant state-of-the-art methods. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Low-rank group sparse coding Denoising model Weighted nonconvex relaxation Iteratively-reweighted algorithm ADMM Image restoration

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Joint group and residual sparse coding for image compressive sensing

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NEUROCOMPUTING 2020年 405卷 72-84页

作者： Li, Lizhao Xiao, Song Zhao, Yimin Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China

Nonlocal self-similarity and group sparsity have been widely utilized in image compressive sensing (CS). Most existing methods utilize either internal priors or external priors. However, when the sampling rate is low, the internal prior information of degraded images is not enough for accurate restoration, resulting in loss of image edges and details. And external prior, which learned from clean data, may be not adaptive to the images. In this paper, we propose a joint group and residual sparse coding method for CS image recovery (JGRSC-CS). In the proposed JGRSC-CS, patch group is treated as the basic unit of sparse coding. Different from the idea of encoding a group with a single dictionary (e.g. DCT, Curvelet or K-SVD dictionary), two dictionaries (namely internal and external dictionaries) are applied to exploit the sparse representation of each group simultaneously. The internal self-adaptive dictionary is used to remove artifacts, and an external Gaussian Mixture Model (GMM) dictionary, learned from clean training images, is used to enhance details and texture. To make the proposed method effective and robust, the split Bregman method is adopted to reconstruct the whole image. Experimental results manifest that with acceptable computational complexity and good convergence property, the proposed JGRSC-CS algorithm outperforms existing state-of-the-art methods in both peak signal to noise ratio (PSNR) and visual quality.

关键词： Compressive sensing group sparse coding Nonlocal self-similarity Gaussian mixture model Split Bregman

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Locality regularized group sparse coding for action recognition

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COMPUTER VISION AND IMAGE UNDERSTANDING 2017年 158卷 106-114页

作者： Bagheri, Mohammad Ali Gao, Qigang Escalera, Sergio Moeslund, Thomas B. Ren, Huamin Etemad, Elham Univ Latestan Dept Engn Latestan Iran Shiraz Univ Technol Dept Ind Engn Shiraz Iran Dalhousie Univ Fac Comp Sci Halifax NS Canada UB Dept Math & Comp Sci Barcelona Spain UAB Comp Vis Ctr Barcelona Spain Aalborg Univ Aalborg Denmark

Bag of visual words (BoVW) models are widely utilized in image/video representation and recognition. The cornerstone of these models is the encoding stage, in which local features are decomposed over a codebook in order to obtain a representation of features. In this paper, we propose a new encoding algorithm by jointly encoding the set of local descriptors of each sample and considering the locality structure of descriptors. The proposed method takes advantages of locality coding such as its stability and robustness to noise in descriptors, as well as the strengths of the group coding strategy by taking into account the potential relation among descriptors of a sample. To efficiently implement our proposed method, we consider the Alternating Direction Method of Multipliers (ADMM) framework, which results in quadratic complexity in the problem size. The method is employed for a challenging classification problem: action recognition by depth cameras. Experimental results demonstrate the outperformance of our methodology compared to the state-of-the-art on the considered datasets. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Bag of words Feature encoding Locality constrained coding group sparse coding Alternating direction method of multipliers Action recognition

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Affine-Constrained group sparse coding Based on Mixed Norm 24th

Affine-Constrained Group Sparse Coding Based on Mixed Norm

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24th International Conference on Neural Information Processing (ICONIP)

作者： Zhang, Jianshu Chen, Zhongyu Tan, Changbing Lin, Feilong Yang, Jie Zheng, Zhonglong Zhejiang Normal Univ Dept Comp Sci Jinhua Zhejiang Peoples R China Shanghai Jiao Tong Univ Inst Pattern Recognit & Image Anal Shanghai Peoples R China

ISBN: (纸本)9783319701363;9783319701356

Recently, sparse coding has received an increasing amount of interests. In this paper, a new algorithm named affine-constrained group sparse coding based on mixed norm (MNACGSC) is presented, which further extends the framework of sparse representation-based classification (SRC). From the perspective of geometry, affine-constrained group sparse coding based on mixed norm (MNACGSC) not only finds out the vector that can be best edcoded according to the given dictionary in the convex hull spanned by input samples, but also establishes on multiple regularization terms which can leverage the collaborative effects of those regularization terms to strength the robustness. This paper mainly discusses L-1-norm and L-2-norm. The experimental results have demonstrated that the proposed model is effective, robust to noise and outperforms some representative methods.

关键词： Affine-constrained group sparse coding Mixed norm

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3D Model Retrieval with Weighted Locality-constrained group sparse coding

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NEUROCOMPUTING 2015年第Part2期151卷 620-625页

作者： Wang, Xiangyu Nie, Weizhi Inst Infocomm Res Singapore Singapore Tianjin Univ Sch Elect Informat Engn Tianjin Peoples R China

In recent years, we have witnessed a flourishing of 3D object modelling. Efficient and effective 3D model retrieval algorithms are high desired and attracted intensive research attentions. In this work, we propose a view-based 3D model retrieval algorithm based on weighted locality-constrained group sparse coding. Representative views are first selected by clustering and the corresponding weights are provided by considering the relationship among these views. By grouping the views from 3D models, a locality-constrained group sparse coding method is employed to find the reconstruction residual for each query view. The distance between query model and candidate model is taken as the weighted sum of residual. The query model is matched to the model which can best reconstruct the query model. Experimental comparisons have been conducted on the ETH 3D model dataset, and the results have demonstrated the effectiveness of the proposed method. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Locality-constrained group sparse coding 3D model retrieval

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Low-rank decomposition and Laplacian group sparse coding for image classification

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NEUROCOMPUTING 2014年 135卷 339-347页

作者： Zhang, Lihe Ma, Chen Dalian Univ Technol Sch Informat & Commun Engn Liaoning 116023 Peoples R China

This paper presents a novel image classification framework (referred to as LR-LGSC) by leveraging the low-rank matrix decomposition and Laplacian group sparse coding. First, motivated by the observation that local features (such as SIFT) extracted from neighboring patches in an image usually contain correlated (or common) items and specific (or noisy) items, we construct a structured dictionary based on the low-rank and sparse components of local features. This dictionary has more powerful representation capability. Then, we investigate group generation for group sparse coding and introduce a Laplacian constraint to take into account the interrelation among groups, which can maintain low reconstruction errors while prompting similar samples to have similar codes. Finally, linear SVM classifier is used for the classification. The proposed method is tested on Caltech-101, UIUC-sports and Scene 15 dataset, and achieves competitive or better results than the state-of-the-art methods. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Image classification Low-rank matrix decomposition group sparse coding Laplacian constraint

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Regularization by double complementary priors for full waveform inversion

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COMPUTERS & GEOSCIENCES 2025年 194卷

作者： Qi, Hongyu Fu, Zhenwu Li, Yang Han, Bo Li, Longsuo Harbin Inst Technol Dept Math Harbin 150001 Peoples R China Quanzhou Univ Informat Engn Dept Math Quanzhou 362000 Peoples R China

Full-waveform inversion (FWI) represents an advanced geophysical imaging technique focused on intricately depicting subsurface physical properties by iteratively minimizing the differences between the simulated and observed seismograms. Unfortunately, the conventional FWI utilizing a least-squares loss function suffers from various drawbacks, including the challenge of local minima and the necessity for human intervention in parameter fine-tuning. It is particularly problematic when handling noisy data and inadequate initial models. Recent works have exhibited promising performance in two-dimensional FWI by integrating structural sparse representation to procure adaptive dictionaries. Drawing inspiration from the competitiveness of structural sparse representation, we introduce a paradigm of group sparse residuals that integrates two types of complementary prior information by harnessing both the internal and external subsurface media models. The proposed algorithm is based on an alternate minimization algorithm to guarantee workflow flexibility and efficient optimization capabilities. We experimentally validate our method for two baseline geological models, and a comparison of the results demonstrates that the proposed algorithm faithfully recovers the velocity models and consistently outperforms other traditional or learning-based algorithms. A further benefit from the group sparse coding used in this method is that it reduces the sensitivity to data noise.

关键词： Full waveform inversion (FWI) group sparse coding Data-driven regularization Double complementary prior Alternating direction method of multipliers (ADMM)

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Locality-constrained group lasso coding for microvessel image classification

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PATTERN RECOGNITION LETTERS 2020年 130卷 132-138页

作者： Chen, Juan Zhou, Shijie Kang, Zhao Wen, Quan Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Sichuan Peoples R China Univ Elect Sci & Technol China Glasgow Coll Chengdu 611731 Sichuan Peoples R China

Image-based classification of histology sections plays an important role in predicting clinical outcomes. In this paper, we propose a Locality-Constrained group Lasso coding (LCGLC) method for microvessel image classification, which realizes the automatic "hot spot" detection of angiogenesis for human liver carcinoma. First, we extract Scale-Invariant Feature Transform (SIFT) descriptors on the Single-Opponent (SO) feature map, which simulates the biological functionality of human visual systems. Then, we present the feature-biased dictionary learning to effectively generate the dictionary of SIFT descriptors. With the learned dictionary, our LCGLC method introduces the locality constraint in classical group lasso problem to encode SIFT descriptors. Furthermore, we apply the Spatial Pyramid Matching (SPM) for the code pooling of microvessel images. Finally, we use Support Vector Machine (SVM) to classify a tissue image as having angiogenesis or not. Comprehensive experiments on the microvessel dataset show that the proposed LCGLC method achieves better performance compared with other representative approaches. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Image classification group sparse coding Microvessel

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