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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Gomes, Yanis Truong, Charles Saut, Jean-Philippe Hafid, Fikri Prieur, Pascale Oudre, Laurent Université Paris Saclay Université Paris Cité ENS Paris Saclay CNRS SSA INSERM Centre Borelli Gif-sur-YvetteF-91190 France Eurobios Mews Labs CachanF-94230 France RTE Direction R&D Paris La DéfenseF-92073 France

ISBN: (纸本)9798350368741

Finding patterns in time series is crucial to understanding physical or physiological phenomena monitored with sensors. convolutional sparse coding (CSC) methods, which approximate signals by a sparse combination of short signal templates (also called atoms), are well-suited for this task. Nevertheless, sparsity results in intractable non-convex optimization problems. This paper introduces an algorithm, based on Multipath Matching Pursuit, which is novel in convolutional settings, to efficiently and accurately estimate atoms' localizations in time series. We describe a principled way to improve this greedy procedure by returning several candidate solutions instead of one. Our approach yields better localization and signal reconstruction on simulated data and in a real-world use case, which consists in automatically detect damages, such as cracks and broken wires, in overhead power lines. © 2025 IEEE.

关键词： compressed sensing convolutional sparse coding deconvolution greedy optimization orthogonal matching pursuit sparse coding sparse representation

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An improved theoretical analysis of convolutional sparse coding using stripe coherence

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2025年 469卷

作者： Li, Haifeng Chen, Wengu Henan Normal Univ Sch Math & Stat Xinxiang 453007 Peoples R China Inst Appl Phys & Computat Math Beijing 100088 Peoples R China

convolutional sparse coding has proven to be very effective in a wide range of applications. Recent work addressed the theoretical performance of global orthogonal matching pursuit (OMP) using the stripe coherence that is a stronger characterization of the convolutional dictionary. In this paper, we further provide the theoretical analysis of convolutional sparse model. Our result improves the guarantee condition for global OMP based on convolutional sparse model. We also provide the theoretical guarantee for basis pursuit using stripe coherence based on convolutional sparse model, which is absent in the existing results.

关键词： convolutional sparse coding sparse signal recovery Stripe coherence Stability guarantees

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Stable and Lightweight Deep Primal-Dual Unrolling for Constrained Image Restoration with convolutional sparse coding

Stable and Lightweight Deep Primal-Dual Unrolling for Constr...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ueki, Takafumi Naganuma, Kazuki Ono, Shunsuke Dept. Computer Science Institute of Science Tokyo Tokyo Japan

ISBN: (纸本)9798350368741

This paper proposes an image restoration method using a convolutional sparse coding (CSC) unrolling network with a box constraint and total variation. Unlike conventional deep unrolling methods, the proposed method constructs an interpretable lightweight network with restoration stability. Specifically, we design a new constrained convex optimization problem that incorporates CSC, a box constraint, and total variation (TV). The box constraint ensures that the image values fall within a certain range, making the restoration process stable. In addition, combining total variation and CSC leads to high interpretability and representation with a small number of parameters. We develop an optimization algorithm based on the primal-dual splitting (PDS) method. Then, by unrolling the algorithm, we construct the proposed lightweight network. Experimental results demonstrate the superiority of the proposed method in image restoration accuracy and lightweightness of the proposed network in the number of parameters. © 2025 IEEE.

关键词： box constraint convolutional sparse coding deep unrolling Image restoration primal-dual splitting algorithm

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convolutional sparse coding for Trajectory Reconstruction

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2015年第3期37卷 529-540页

作者： Zhu, Yingying Lucey, Simon Commonwealth Sci & Ind Res Org Brisbane Qld 4069 Australia Univ Queensland Brisbane Qld 4067 Australia Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA

Trajectory basis Non-Rigid Structure from Motion (NRSfM) refers to the process of reconstructing the 3D trajectory of each point of a non-rigid object from just their 2D projected trajectories. Reconstruction relies on two factors: (i) the condition of the composed camera & trajectory basis matrix, and (ii) whether the trajectory basis has enough degrees of freedom to model the 3D point trajectory. These two factors are inherently conflicting. Employing a trajectory basis with small capacity has the positive characteristic of reducing the likelihood of an ill-conditioned system (when composed with the camera) during reconstruction. However, this has the negative characteristic of increasing the likelihood that the basis will not be able to fully model the object's "true" 3D point trajectories. In this paper we draw upon a well known result centering around the Reduced Isometry Property (RIP) condition for sparse signal reconstruction. RIP allow us to relax the requirement that the full trajectory basis composed with the camera matrix must be well conditioned. Further, we propose a strategy for learning an over-complete basis using convolutional sparse coding from naturally occurring point trajectory corpora to increase the likelihood that the RIP condition holds for a broad class of point trajectories and camera motions. Finally, we propose an l(1) inspired objective for trajectory reconstruction that is able to "adaptively" select the smallest sub-matrix from an over-complete trajectory basis that balances (i) and (ii). We present more practical 3D reconstruction results compared to current state of the art in trajectory basis NRSfM.

关键词： Nonrigid structure from motion convolutional sparse coding l(0) norm l(1) norm reconstructability

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convolutional sparse coding for RGB plus NIR Imaging

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2018年第4期27卷 1611-1625页

作者： Hu, Xuemei Heide, Felix Dai, Qionghai Wetzstein, Gordon Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Stanford Univ Dept Elect Engn Stanford CA 94306 USA Zhejiang Future Technol Inst Jiaxing 314006 Peoples R China

Emerging sensor designs increasingly rely on novel color filter arrays (CFAs) to sample the incident spectrum in unconventional ways. In particular, capturing a near-infrared (NIR) channel along with conventional RGB color is an exciting new imaging modality. RGB+NIR sensing has broad applications in computational photography, such as low-light denoising, it has applications in computer vision, such as facial recognition and tracking, and it paves the way toward low-cost single-sensor RGB and depth imaging using structured illumination. However, cost-effective commercial CFAs suffer from severe spectral cross talk. This cross talk represents a major challenge in high-quality RGB+NIR imaging, rendering existing spatially multiplexed sensor designs impractical. In this work, we introduce a new approach to RGB+NIR image reconstruction using learned convolutional sparse priors. We demonstrate high-quality color and NIR imaging for challenging scenes, even including high-frequency structured NIR illumination. The effectiveness of the proposed method is validated on a large data set of experimental captures, and simulated benchmark results which demonstrate that this work achieves unprecedented reconstruction quality.

关键词： Computational photography convolutional sparse coding structured illumination

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convolutional sparse coding Using Pathfinder Algorithm-Optimized Orthogonal Matching Pursuit With Asymmetric Gaussian Chirplet Model in Bearing Fault Detection

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IEEE SENSORS JOURNAL 2021年第16期21卷 18132-18145页

作者： Zhou, Qiuyang Zhang, Yuhui Yi, Cai Lin, Jianhui He, Liu Hu, Qiwei Southwest Jiaotong Univ State Key Lab Tract Power Chengdu 610031 Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 260061 Peoples R China China Railway Elect Survey Design & Res Inst Co L Tianjin 300250 Peoples R China

sparse representation has been widely used in bearing fault impact detection, which can find the impact that best matches the fault waveform from the pre-defined dictionary and recover the fault impulse waveform. However, the current dictionary of sparse representation and the efficiency of sparse representation algorithm need to be improved. In order to accurately detect the fault impulse in the original signal, a convolutional sparse coding using pathfinder algorithm-optimized orthogonal matching pursuit with asymmetric Gaussian chirplet model (CSC-OAGCM) is proposed in this paper. A new time-frequency atom prototype, AGCM, is used to match the fault impulse waveform. The specific application steps of the proposed algorithm are as follows: Firstly, a convolution dictionary is constructed with atoms generated by AGCM. Subsequently, based on the convolution dictionary, a pathfinder algorithm-optimized orthogonal matching pursuit algorithm is used to solve the sparse representation and optimize the atomic parameters to achieve the best approximation of the original signal. In other words, the proposed method detects the convolutional sparse patterns in the signal. A simulation signal, two sets of mixed signals of experimental data collected from the experimental platform and an axle box vibration signal collected from the actual operating train are used to verify the effectiveness of proposed method. Additionally, the spectral kurtosis and empirical wavelet transform are also used to process these signals, and their processing results are compared with those obtained by the proposed method to demonstrate the superiority of the proposed method.

关键词： Dictionaries Convolution Matching pursuit algorithms Fault diagnosis Vibrations convolutional codes Encoding Bearing fault diagnosis convolutional sparse coding asymmetric Gaussian chirplet model pathfinder algorithm orthogonal matching pursuit

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convolutional sparse coding for Compressed Sensing CT Reconstruction

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2019年第11期38卷 2607-2619页

作者： Bao, Peng Xia, Wenjun Yang, Kang Chen, Weiyan Chen, Mianyi Xi, Yan Niu, Shanzhou Zhou, Jiliu Zhang, He Sun, Huaiqiang Wang, Zhangyang Zhang, Yi Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Peking Univ Sch Elect Engn & Comp Sci Minist Educ Key Lab High Confidence Software Technol Beijing 100871 Peoples R China Shanghai First Imaging Informat Technol Co Ltd Shanghai 200120 Peoples R China Gannan Normal Univ Sch Math & Comp Sci Ganzhou 341000 Peoples R China Rutgers State Univ New Brunswick NJ 08854 USA West China Hosp Sichuan Univ Dept Radiol Chengdu 610041 Sichuan Peoples R China Texas A&M Univ Dept Comp Sci & Engn College Stn TX 77843 USA

Over the past few years, dictionary learning (DL)-based methods have been successfully used in various image reconstruction problems. However, the traditional DL-based computed tomography (CT) reconstruction methods are patch-based and ignore the consistency of pixels in overlapped patches. In addition, the features learned by these methods always contain shifted versions of the same features. In recent years, convolutional sparse coding (CSC) has been developed to address these problems. In this paper, inspired by several successful applications of CSC in the field of signal processing, we explore the potential of CSC in sparse-view CT reconstruction. By directly working on the whole image, without the necessity of dividing the image into overlapped patches in DL-based methods, the proposed methods can maintain more details and avoid artifacts caused by patch aggregation. With predetermined filters, an alternating scheme is developed to optimize the objective function. Extensive experiments with simulated and real CT data were performed to validate the effectiveness of the proposed methods. The qualitative and quantitative results demonstrate that the proposed methods achieve better performance than the several existing state-of-the-art methods.

关键词： Image reconstruction Computed tomography convolutional codes Dictionaries TV Image coding Compressed sensing Computed tomography compressed sensing CT reconstruction convolutional sparse coding

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convolutional sparse coding Based Channel Estimation for OTFS-SCMA in Uplink

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IEEE TRANSACTIONS ON COMMUNICATIONS 2022年第8期70卷 5241-5257页

作者： Thomas, Anna Deka, Kuntal Raviteja, Patchava Sharma, Sanjeev Indian Inst Technol Goa Sch Elect Sci Ponda 403401 India Indian Inst Technol Guwahati Dept EEE Gauhati 781039 India Qualcomm San Diego CA 92121 USA Indian Inst Technol BHU Dept Elect Engn Varanasi 221005 Uttar Pradesh India

Orthogonal time frequency space (OTFS) has emerged as the most sought-after modulation technique in a high mobility scenario. sparse code multiple access (SCMA) is an attractive code-domain non-orthogonal multiple access (NOMA) technique. Recently a code-domain NOMA approach for OTFS, named OTFS-SCMA, is proposed. OTFS-SCMA is a promising framework that meets the demands of high mobility and massive connectivity. This paper presents a channel estimation technique based on the convolutional sparse coding (CSC) approach for OTFS-SCMA in the uplink. The channel estimation task is formulated as a CSC problem following a careful rearrangement of the OTFS input-output relation. We use an embedded pilot-aided sparse-pilot structure that enjoys the features of both OTFS and SCMA. The existing channel estimation techniques for OTFS in multi-user scenarios for uplink demand extremely high overhead for pilot and guard symbols, proportional to the number of users. The proposed method maintains a minimal overhead equivalent to a single user without compromising on the estimation error. The results show that the proposed channel estimation algorithm is very efficient in bit error rate (BER), normalized mean square error (NMSE), and spectral efficiency (SE).

关键词： Channel estimation Symbols NOMA Uplink Modulation OFDM Doppler effect OTFS SCMA NOMA channel estimation compressive sensing convolutional sparse coding

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convolutional sparse coding CLASSIFICATION MODEL FOR IMAGE CLASSIFICATION 23

CONVOLUTIONAL SPARSE CODING CLASSIFICATION MODEL FOR IMAGE C...

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23rd IEEE International Conference on Image Processing (ICIP)

作者： Chen, Boheng Li, Jie Ma, Biyun Wei, Gang South China Univ Technol Sch Elect & Informat Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781467399616

In this paper, we present a novel classification model which combines the convolutional sparse coding framework with the classification strategy. In the training phase, the proposed model trained a convolutional filter bank by all images of each class. In the test phase, the label of test image is determined by all convolutional filter banks. Compared with canonical sparse representation and dictionary learning classification algorithm, more representative information of the corresponding images could be captured by the trained filters, thus better classification performance can be obtained. Experimental results on some image benchmark databases demonstrated the effectiveness of the proposed method.

关键词： convolutional sparse coding sparse representation convolutional filter image representation image classification

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convolutional sparse coding for Face Recognition

Convolutional Sparse Coding for Face Recognition

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4th International Conference on Information, Cybernetics and Computational Social Systems (ICCSS)

作者： Jin, Junwei Chen, C. L. Philip Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Macau Peoples R China

ISBN: (纸本)9781538632574

Face recognition has been an important task in pattern recognition and computer vision. Recently, sparse representation has become a popular data representation method in face recognition field. convolutional sparse coding, which replaces the linear combination of a set of dictionary atoms with the sum of s series of mapping term convoluted with the dictionary filters, was proposed to improve the application performance of traditional sparse coding. In this paper, we apply this convolutional sparse coding method to do the face recognition. As the trained dictionary filters could capture more discriminative information in the corresponding face images, it could believed that better classification performance can be achieved. Experimental results on the face image database demonstrated the novel convolutional sparse coding algorithm can achieve better recognition rate.

关键词： face recognition convolutional sparse coding filters sparse representation image classification

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