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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Hillel Sreter Raja Giryes School of Electrical Engineering Tel Aviv University Tel Aviv Israel

ISBN: (纸本)9781538646595

We propose a convolutional recurrent sparse auto-encoder model. The model consists of a sparse encoder, which is a convolutional extension of the learned ISTA (LISTA) method, and a linear convolutional decoder. Our strategy offers a simple strategy for learning a task-driven sparse convolutional dictionary (CD), and producing an approximate convolutional sparse code (CSC) over the learned dictionary. We trained the model to minimize reconstruction loss via gradient decent with back-propagation and have achieved competitve results to KSVD image denoising and to leading CSC methods in image inpainting requiring only a small fraction of their runtime.

关键词： sparse coding ISTA LISTA convolutional sparse coding Neural networks sparse coding Neural network Dictionaries as Topic Runtime

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convolutional sparse-coding-based 3DV image super-resolution framework

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JOURNAL OF OPTICS-INDIA 2024年第5期53卷 4930-4942页

作者： El-Shafai, Walid Abd El-Fatah, Mohamed A. Dessouky, Moawad I. Khalaf, Ashraf A. M. Abd El-Samie, Fathi E. Prince Sultan Univ Dept Comp Sci Secur Engn Lab Riyadh 11586 Saudi Arabia Menoufia Univ Fac Elect Engn Dept Elect & Elect Commun Engn Menoufia 32952 Egypt Minia Univ Fac Engn Dept Elect & Elect Commun Engn Al Minya 61111 Egypt Minia Univ Fac Engn Dept Elect Engn Al Minya Egypt Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia

Super-Resolution (SR) reconstruction of images has an extreme importance for vision applications. Numerous algorithms have been introduced for this purpose in recent years. This paper presents a cost-effective approach for visual quality and resolution enhancement of 3D Video (3DV) sequences. The basic idea of this approach is to employ a deep learning algorithm for SR reconstruction based on sparse coding (SC) applied on video sequences degraded with up-sampling, blurring, and noise. The proposed approach is compared with the state-of-the-art bicubic approach. Simulation results have revealed high quality of the obtained 3DV frames with the proposed approach with appreciated outcomes of local contrast, average gradient, Mean Square Error (MSE), edge intensity, Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), and entropy metrics. In addition, the simulation results introduce good and appreciated histogram results that prove the performance efficiency of the proposed SR reconstruction framework.

关键词： 3DV SR convolutional sparse coding Bicubic approach Reconstruction process Deep learning Quality metrics

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convolutional Analysis sparse coding for Multimodal Image Super-Resolution

IEEE SENSORS LETTERS

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IEEE SENSORS LETTERS 2024年第6期8卷 1页

作者： Kumar, Kriti Majumdar, Angshul Kumar, A. Anil Chandra, M. Girish TCS Res Bangalore 560066 India Indraprastha Inst Informat Technol Delhi New Delhi 110020 India TCG CREST Inst Adv Intelligence Kolkata 700091 India

With multimodal imaging systems in place, recent research focus has been directed toward exploiting the knowledge from different imaging modalities to solve inverse problems, one example being image super-resolution, considered in this letter. The goal of multimodal image super-resolution (MISR) is to enhance the resolution of low-resolution (LR) images from the target modality by taking guidance from high-resolution (HR) images from a different modality. Conventional methods for MISR employing convolutional neural networks typically adopt an encoder-decoder architecture that often demands massive data for optimal reconstruction and tends to overfit in data-limited scenarios. The proposed work presents a convolutional analysis sparse coding-based method, employing convolutional transforms that eliminate the need for learning the decoder network and learns unique filters. This reduces the number of trainable parameters, making it suitable for learning with limited data. A joint optimization formulation is presented that learns dedicated convolutional transforms for both the LR images of target modality and the HR images of guidance modality, and a fusing (combining) transform that combines the respective transform features to reconstruct the HR images of target modality. Unlike the dictionary-based synthesis sparse coding methods for MISR, the proposed approach offers enhanced performance with reduced complexity due to some of the inherent advantages of transform learning. The effectiveness of the proposed method is validated using RGB-Near Infrared and RGB-Multispectral datasets. Experimental findings demonstrate enhanced reconstruction performance achieved by the proposed method, compared with the state-of-the-art techniques. Moreover, the proposed method demonstrates improved robustness to noise compared with other sparse coding-based methods.

关键词： Transforms convolutional codes Image reconstruction Superresolution Standards Benchmark testing Task analysis Sensor applications convolutional sparse coding convolutional transform learning (CTL) joint optimization multimodal image super-resolution (MISR)

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convolutional Laplacian sparse coding

Convolutional Laplacian Sparse Coding

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IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)

作者： Luo, Xiyang Wohlberg, Brendt Univ Calif Los Angeles Dept Math Los Angeles CA 90024 USA Los Alamos Natl Lab Div Theoret Los Alamos NM USA

ISBN: (纸本)9781467399197

We propose to extend the the standard convolutional sparse representation by combining it with a non-local graph Laplacian term. This additional term is chosen to address some of the deficiencies of the l(1) norm in regularizing these representations, and is shown to have an advantage in both dictionary learning and an example image reconstruction problem.

关键词： sparse Representation convolutional sparse coding Laplacian sparse coding

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convolutional neural networks analyzed via convolutional sparse coding

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2017年第1期18卷

作者： Vardan Papyan Yaniv Romano Michael Elad Department of Computer Science Technion - Israel Institute of Technology Technion City Haifa Israel

convolutional neural networks (CNN) have led to many state-of-the-art results spanning through various fields. However, a clear and profound theoretical understanding of the forward pass, the core algorithm of CNN, is still lacking. In parallel, within the wide field of sparse approximation, convolutional sparse coding (CSC) has gained increasing attention in recent years. A theoretical study of this model was recently conducted, establishing it as a reliable and stable alternative to the commonly practiced patch-based processing. Herein, we propose a novel multi-layer model, ML-CSC, in which signals are assumed to emerge from a cascade of CSC layers. This is shown to be tightly connected to CNN, so much so that the forward pass of the CNN is in fact the thresholding pursuit serving the ML-CSC model. This connection brings a fresh view to CNN, as we are able to attribute to this architecture theoretical claims such as uniqueness of the representations throughout the network, and their stable estimation, all guaranteed under simple local sparsity conditions. Lastly, identifying the weaknesses in the above pursuit scheme, we propose an alternative to the forward pass, which is connected to deconvolutional and recurrent networks, and also has better theoretical guarantees.

关键词： basis pursuit convolutional neural networks convolutional sparse coding deep learning forward pass sparse representation thresholding algorithm

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MDSC-Net: Multi-Modal Discriminative sparse coding Driven RGB-D Classification Network

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IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 442-454页

作者： Xu, Jingyi Deng, Xin Fu, Yibing Xu, Mai Li, Shengxi Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Beihang Univ Shen Yuan Honors Coll Beijing 100191 Peoples R China

In this paper, we propose a novel sparsity-driven deep neural network to solve the RGB-D image classification problem. Different from existing classification networks, our network architecture is designed by drawing inspirations from a new proposed multi-modal discriminative sparse coding (MDSC) model. The key feature of this model is that it can gradually separate the discriminative and non-discriminative features in RGB-D images in a coarse-to-fine manner. Only the discriminative features are integrated and refined for classification, while the non-discriminative features are discarded, to improve the classification accuracy and efficiency. Derived from the MDSC model, the proposed network is composed of three modules, i.e., the shared feature extraction (SFE) module, discriminative feature refinement (DFR) module, and classification module. The architecture of each module is derived from the optimization solution in the MDSC model. To the best of our knowledge, this is the first time a fully sparsity-driven network has been proposed for RGB-D image classification. Extensive results verify the effectiveness of our method on different RGB-D image datasets.

关键词： Feature extraction Image coding Image classification Dictionaries Optimization Classification algorithms Deep learning convolutional neural networks Accuracy Encoding convolutional sparse coding RGB-D image classification Discriminative features

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Learning to Generalize Heterogeneous Representation for Cross-Modality Image Synthesis via Multiple Domain Interventions

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2025年 1-22页

作者： Huang, Yawen Huang, Huimin Zheng, Hao Li, Yuexiang Zheng, Feng Zhen, Xiantong Zheng, Yefeng Tencent Jarvis Res Ctr YouTu Lab Shenzhen Peoples R China Guangxi Med Univ Guangxi Key Lab Genom & Personalized Med Med AI Res MARS Grp Nanning 530021 Guangxi Peoples R China Southern Univ Sci & Technol Shenzhen Peoples R China United Imaging Healthcare Co Ltd Cent Res Inst Beijing Peoples R China Westlake Univ Med Artificial Intelligence Lab Hangzhou Peoples R China

Magnetic resonance imaging with modality diversity substantially increases productivity in routine diagnosis and advanced research. However, high inter-equipment variability and expensive examination cost remain as key challenges in acquiring and utilizing multi-modal images. Missing modalities often can be synthesized from existing ones. While the rapid growth in image style transfer with deep models overwhelms the above endeavor, such image synthesis may not always be achievable and even impractical when applied to medical data. The proposed method addresses this issue by a convolutional sparse coding (CSC) adaptation network to handle the lacking of generalizing medical image representation learning. We reduce both inter-domain and intra-domain divergences by the domain-adaptation and domain-standardization modules, respectively. On the basis of CSC features, we penalize their subspace mismatching to reduce the generalization error. The overall framework is cast in a minimax setting, and the extensive experiments show that the proposed method yields state-of-the-art results on multiple datasets.

关键词： Representation learning Cross-moality images convolutional sparse coding Image synthesis

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A deep learning approach for pose error prediction in parallel robots

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MEASUREMENT 2025年第PartA期242卷

作者： Zhang, Han Zhu, Xin Yang, Ming Liu, Zhihua Cai, Chenguang Natl Inst Metrol Inst Mech & Acoust Beijing 100029 Peoples R China Guizhou Univ Coll Elect Engn Guiyang 550025 Peoples R China

Parallel robots are increasingly favored in industrial automation due to their high precision, stability, and rigidity in high-speed and heavy-load tasks. However, factors such as manufacturing tolerances, assembly deviations, loads, and environmental conditions can lead to pose inaccuracies, manifesting as both geometric and non-geometric errors. Traditional model-driven approaches effectively predict geometric errors but often neglect non-geometric factors, potentially impacting the overall accuracy. This paper presents a deep learning-based method for comprehensive pose error prediction in parallel robots, addressing both geometric and non-geometric errors. The proposed approach employs an encoder-decoder network that takes target pose inputs and outputs predicted pose errors. The encoder integrates multi-layer convolutional sparse coding (CSC) blocks and efficient channel attention (ECA) modules to extract features from the network inputs. Monte Carlo dropout is utilized to estimate prediction uncertainties, enhancing the reliability of the network predictions. Experimental validation on a Stewart platform demonstrates the high accuracy and robustness of the method under various loading conditions. Comparative analyses with several typical models affirm the superior performance of the proposed network, indicating its potential to improve the operational accuracy and reliability of parallel robots in high-precision applications.

关键词： Parallel robots Pose error prediction Deep learning convolutional sparse coding Monte Carlo dropout

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Efficient convolutional Forward Modeling and sparse coding in Multichannel Imaging 32

Efficient Convolutional Forward Modeling and Sparse Coding i...

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32nd European Signal Processing Conference (EUSIPCO)

作者： Wang, Han Kvich, Yhonatan Perez, Eduardo Roemer, Florian Eldar, Yonina C. Fraunhofer Inst Nondestruct Testing Appl AI Signal Proc & Data Anal Saarbrucken Germany Weizmann Inst Sci Fac Math & Comp Sci Rehovot Israel Tech Univ Ilmenau Dept Elect Measurements & Signal Proc Ilmenau Germany

ISBN: (纸本)9789464593617;9798331519773

This study considers the Block-Toeplitz structural properties inherent in traditional multichannel forward model matrices, using Full Matrix Capture (FMC) in ultrasonic testing as a case study. We propose an analytical convolutional forward model that transforms reflectivity maps into FMC data. Our findings demonstrate that the convolutional model excels over its matrix-based counterpart in terms of computational efficiency and storage requirements. This accelerated forward modeling approach holds significant potential for various inverse problems, notably enhancing sparse Signal Recovery (SSR) within the context LASSO regression, which facilitates efficient convolutional sparse coding (CSC) algorithms. Additionally, we explore the integration of convolutional Neural Networks (CNNs) for the forward model, employing deep unfolding to implement the Learned Block convolutional ISTA (BC-LISTA).

关键词： Forward Modeling convolutional sparse coding Deep Unfolding Toeplitz Matrix Multichannel Imaging

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Poisson2Poisson-sparse: Unsupervised Poisson noise image denoising based on sparse modeling

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SIGNAL PROCESSING 2025年 230卷

作者： Xiao, Lingzhi Wang, Shengbiao Zhang, Jun Wei, Jiuzhe Yang, Shihua Nanjing Univ Sci & Technol Sch Math & Stat Nanjing 210094 Jiangsu Peoples R China Beijing Inst Space Mech & Elect Beijing 100076 Peoples R China

Infields such as low-light photography, astronomical imaging, and low-dose computed tomography scanning, Poisson noise severely degrades image quality due to extremely low photon counts (averaging below one) and their Poisson-distributed statistical characteristics. A recent self-supervised Poisson denoising method uses only a single noisy image to improve image quality. However, it struggles under high Poisson noise due to its denoising model based on Gaussian distribution and suffers from long inference times. To address these issues, we propose an unsupervised Poisson denoising method based on sparse representation. Specifically, we first establish a more accurate sparse representation model based on Poisson distribution to enhance denoising performance. Given the difficulty of solving this model directly, we develop an iterative optimization algorithm using convolutional sparse coding and the alternating direction method of multipliers. Inspired by the unfolding technique, we further reduce computational cost by unfolding the iterative process into a finite-cycle learning network. To overcome the reliance on paired datasets and accelerate inference times, we employ a Poisson loss function, a Neighbor2Neighbor training strategy, and incorporate total variation loss, which together enable unsupervised learning. Experimental results demonstrate that our proposed method significantly outperforms existing unsupervised Poisson denoising methods and achieves high computational efficiency.

关键词： Unsupervised learning Image denoising Poisson noise Unfolding convolutional sparse coding

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