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Compressive Spectral Video Sensing using the convolutional sparse coding framework CSC4D

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2023年第1期92卷

作者： Barajas-Solano, Crisostomo Ramirez, Juan-Marcos Torre, Jose Ignacio Martinez Arguello, Henry Univ Ind Santander Santander Colombia Inst IMDEA Networks Madrid Spain Univ Rey Juan Carlos Madrid Spain

Spectral Videos (SV) contain a scene's spatial-spectral-time information. Just as with Spectral Images (SI), SVs require expensive sensing hardware, storage plus high frame ratios. Although Super Resolution techniques improve the quality of low-resolution SVs, Compressive Spectral Video Sensing (CSVS) senses high-quality SVs by extending the Compressive Sensing Image (CSI) techniques. CSI uses the universal sparse Signal Representation (SSR) model for SVs and SIs despite the limited quality of the recovered signals. On the other hand, dictionaries synthesis models are used successfully for representing SIs, SVs, and in CSI. This work proposes the 4D convolutional sparse representation (CSC4D) for recovering full-resolution SV from CSVS measurements. It is based on a multidimensional formulation of the CSC model, profiting from its robustness without additional optical flow information. Extensive numerical simulations (two CSI architectures and noise models) show that the proposed CSC4D+CSVS improves the state-of-the-art in both quality and border sharpness by up to 1.5 dB.

关键词： Compressive spectral video sensing convolutional sparse coding sparse representation Spectral videos

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Spatial-spectral feature based approach towards convolutional sparse coding of hyperspectral images

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COMPUTER VISION AND IMAGE UNDERSTANDING 2019年 188卷 102797-000页

作者： Arun, P., V Mohan, Krishna B. Porwal, A. Indian Inst Technol CSRE Mumbai 400076 Maharashtra India

The sparse coding approaches, which learn over-complete bases for efficiently representing a given set of data, are being extensively studied for various image analysis tasks. However, since sparse analysis of individual bands does not consider inter-spectral characteristics, adaptation of these approaches to hyperspectral domain has been quite limited. In this regard, we investigate convolutional frameworks for simultaneously learning sparse codes and dictionaries so as to efficiently represent the spectral-spatial characteristics of hypercubes. This study also reviews prominent convolutional sparse coding algorithms relevant to hyperspectral datasets. Unlike conventional approaches, in order to address the issues due to huge spectral dimension and high inter-band correlation, the proposed approaches implement sparse coding in a convolutional encoder- decoder framework. Two architectures, one based on shrinkage thresholding algorithm and other based on structured sparse regularization, are proposed. These architectures adopt 3D convolutions in such a way as to model both spectral and spatial features. Although Architecture-1 gives better results than other prominent approaches, its accuracy deteriorates with increase in spectral dimension of input. This can be attributed to the bottleneck due to shrinkage thresholding operation. To address the issue, Architecture-2 adopts structured sparse regularizations over filter weights and feature tensors. Results indicate that the proposed approaches achieve significant improvement in reconstruction accuracy when compared to the existing approaches. The frameworks are also evaluated with respect to super-resolution and dimensionality reduction of hyperspectral datasets. From different experiments, it is observed that the simultaneous usage of 3D and 2D filters along with sparse regularizations significantly improve learning capability of the networks.

关键词： convolutional sparse coding Hyperspectral convolutional neural network

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Tensor completion via convolutional sparse coding with small samples-based training

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PATTERN RECOGNITION 2023年第1期141卷

作者： Liao, Tianchi Wu, Zhebin Chen, Chuan Zheng, Zibin Zhang, Xiongjun Sun Yat sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Sun Yat sen Univ Sch Software Engn Zhuhai 519000 Peoples R China Cent China Normal Univ Sch Math & Stat Hubei Key Lab Math Sci Wuhan 430079 Peoples R China

Tensor data often suffer from missing value problems due to the complex high-dimensional structure while acquiring them. To complete the missing information, lots of Low-Rank Tensor Completion (LRTC) methods have been proposed, most of which depend on the low-rank property of tensor data. In this way, the low-rank component of the original data could be recovered roughly. However, the shortcoming is that the detailed information can not be fully restored, no matter the Sum of the Nuclear Norm (SNN) nor the Tensor Nuclear Norm (TNN) based methods. On the contrary, in the field of signal processing, convolutional sparse coding (CSC) can provide a good representation of the high-frequency component of the image, which is generally associated with the detail component of the data. To this end, we propose two novel methods, LRTC-CSC-I and LRTC-CSC-II, which adopt CSC as a supplementary regularization for LRTC to capture the high-frequency components. Therefore, the LRTC-CSC methods can not only solve the missing value problem but also recover the details. Moreover, the regularizer CSC can be trained with small samples due to the sparsity characteristic. Extensive experiments show the effectiveness of LRTC-CSC methods, and quantitative evaluation indicates that the performance of our models are superior to state-of-the-art methods. (c) 2023 Elsevier Ltd. All rights reserved.

关键词： Tensor completion convolutional sparse coding High-pass filter Inexact ADMM

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Pan-Sharpening for Spectral Details Preservation Via convolutional sparse coding in Non-Subsampled Shearlet Space

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2023年第2期23卷

作者： Sangani, Dhara J. J. Thakker, Rajesh A. A. Panchal, S. D. Gogineni, Rajesh Vishwakarma Govt Engn Coll ECE Dept Ahmadabad 382424 Gujarat India Gujarat Technol Univ GTU Grad Sch Engn & Technol GSET Ahmadabad 382424 India Gujarat Technol Univ Comp Engn Ahmadabad India Dhanekula Inst Engn & Technol Dept Elect & Commun Engn Vijayawada 521139 India

The optical satellite sensors encounter certain constraints on producing high-resolution multispectral (HRMS) images. Pan-sharpening (PS) is a remote sensing image fusion technique, which is an effective mechanism to overcome the limitations of available imaging products. The prevalent issue in PS algorithms is the imbalance between spatial quality and spectral details preservation, thereby producing intensity variations in the fused image. In this paper, a PS method is proposed based on convolutional sparse coding (CSC) implemented in the non-subsampled shearlet transform (NSST) domain. The source images, panchromatic (PAN) and multispectral (MS) images, are decomposed using NSST. The resultant high-frequency bands are fused using adaptive weights determined from chaotic grey wolf optimization (CGWO) algorithm. The CSC-based model is employed to fuse the low-frequency bands. Further, an iterative filtering mechanism is developed to enhance the quality of fused image. Four datasets with different geographical content like urban area, vegetation, etc. and eight existing algorithms are used for evaluation of the proposed PS method. The comprehensive visual and quantitative results approve that the proposed method accomplishes considerable improvement in spatial and spectral details equivalence in the pan-sharpened image.

关键词： Pan-sharpening non-subsampled shearlet transform convolutional sparse coding chaotic grey wolf optimization iterative filtering high-resolution multispectral image

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Matching Pursuit Based convolutional sparse coding

Matching Pursuit Based Convolutional Sparse Coding

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Elad Plaut Raja Giryes School of Electrical Engineering Tel Aviv University Tel Aviv Israel

ISBN: (纸本)9781538646595

convolutional sparse coding using the l_(0,∞) norm has been described as "a problem that operates locally while thinking globally". In this paper, we present a matching pursuit based greedy algorithm specifically tailored to the l_(0,∞) norm. We also propose a corresponding dictionary learning algorithm, which trains a local dictionary on a set of global images. Our approach is based on the convolutional relationship between the local dictionary and the global image. It operates locally while taking into account the global nature of the images. We demonstrate the usage of our proposed strategy for the task of image inpainting.

关键词： convolutional sparse coding Global modeling Local Processing Greedy Algorithms sparse Representations

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Data Driven convolutional sparse coding for Visual Recognition

Data Driven Convolutional Sparse Coding for Visual Recogniti...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Yijie Zeng Jichao Chen Guang-Bin Huang School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798

ISBN: (纸本)9781538646595

convolutional sparse coding (CSC) has become an important method in image processing and computer vision. In this paper we focus on visual recognition problems and apply CSC as a feature learning method. We propose a task-specific approach to treat the dictionary of CSC as parameters for a larger learning framework. These parameters are differentiable under mild conditions, and could be updated end-to-end using back-propagation when the errors from the task objectives are provided. We perform several experiments to show that such method provides a more discriminate representation compared with previous CSC methods, and this data driven approach is effective for visual recognition problems.

关键词： convolutional sparse coding dictionary learning sparse representation supervised learning visual recognition sparse representation Semi-supervised Learning Visualization Differentiable Dictionaries as Topic end-to-end images computer vision

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Projecting on to the Multi-Layer convolutional sparse coding Model

Projecting on to the Multi-Layer Convolutional Sparse Coding...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Jeremias Sulam Vardan Papyant Yaniv Romano Michael Elad Computer Science Department Electrical Engineering Department Technion - Israel Institute of Technology

ISBN: (纸本)9781538646595

The recently proposed Multi-Layer convolutional sparse coding (ML-CSC) model, consisting of a cascade of convolutional sparse layers, provides a new interpretation of convolutional Neural Networks (CNNs). Under this framework, the forward pass in a CNN is equivalent to an algorithm that estimates nested sparse representation vectors from a given input signal. Despite having served as a pivotal connection between CNNs and sparse modeling, it is still unclear how to develop pursuit algorithms that serve this model exactly. In this work, we propose a new pursuit formulation by adopting a projection approach. We provide new and improved bounds on the stability of the resulting convolutional sparse representations, and we propose a multi-layer projection algorithm to retrieve them. We demonstrate this algorithm numerically, showing that it is superior to the Layered Basis Pursuit alternative in retrieving the representations of signals belonging to the ML-CSC model.

关键词： convolutional sparse coding Multilayer Pursuit Stability Guarantees convolutional Neural Networks

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Adaptive convolutional sparse coding with Weighted l1 Norm

Adaptive Convolutional Sparse Coding with Weighted l1 Norm

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第三十八届中国控制会议

作者： Jiale Qiu Min Yang College Of Automation & College Of Artificial Intelligence Nanjing University of Posts and Telecommunications

convolutional sparse coding computes a sparse representation for an entire image by modeling the signal as a sum of several convolutions of dictionary filters and sparse feature maps, instead of the linear combination of dictionary vectors in standard sparse coding. Most of the existing algorithms use a invariant regularization parameter, which can hardly deal well with varying local contrast. In this paper, we propose a new method Adaptive convolutional sparse coding(ACSC) by introducing a set of weight vectors into the 1 regularization term to set different penalty coefficients for different sparse feature maps, which can better adapt with the varying local sparse requirements of images. Experimental results show that the proposed ACSC is superior to the existing convolutional sparse coding algorithm in image reconstruction.

关键词： sparse coding convolutional sparse coding ADMM Image Reconstruction

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Single Image Super-Resolution via convolutional sparse coding with One Group of Filters 2019

Single Image Super-Resolution via Convolutional Sparse Codin...

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Proceedings of the 2019 3rd International Conference on Computer Science and Artificial Intelligence

作者： Mingming Bai Yunjie Zhang BinZhang Department of Mathematics Dalian Maritime University Dalian China

ISBN: (纸本)9781450376273

Using the characteristic that image patches can be represented sparsely on a selected over-complete dictionary, the sparse coding based super-resolution (ScSR) method pioneers a new avenue for the SR technology. However, ScSR will result in obvious block-effect because of the block preprocessing of the image. For this purpose, Zeiler et al. proposed convolutional sparse coding based SR (CSC-SR) method to solve this problem. Because it needs to learn two groups of filters and mapping function between HR and LR feature maps, CSC-SR usually puts forward high demand to storage and relatively large the calculation expense. In this paper, we propose a modified CSC-SR method to single image super-resolution. The proposed method only needs to train one group of filers, and explores directly the mapping function by least squares algorithm, which can effectively reduce the workload of model training. Experimental results for different test images show that our model has achieved effective improvement over the previous methods.

关键词： sparse representation Super-resolution convolutional sparse coding

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Fast convolutional sparse coding with separable filters

Fast convolutional sparse coding with separable filters

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Gustavo Silva Jorge Quesada Paul Rodriguez Brendt Wohlberg Department of Electrical Engineering Pontificia Universidad Catolica del Peru Lima Peru T-5 Applied Mathematics and Plasma Physics Los Alamos National Laboratory NM 87545 USA

ISBN: (纸本)9781509041183

convolutional sparse representations (CSR) of images are receiving increasing attention as an alternative to the usual independent patch-wise application of standard sparse representations. For CSR the dictionary is a filter bank of non-separable 2D filters, and the representation itself can be viewed as the synthesis dual of the analysis representation provided by a single level of a convolutional neural network (CNN). The current state-of-the-art convolutional sparse coding (CSC) algorithms achieve their computational efficiency by applying the convolutions in the frequency domain. It has been shown that any given 2D non-separable filter bank can be approximated as a linear combination of a relatively small number of separable filters. This approximation has been exploited for computationally efficient CNN implementations, but has thus far not been considered for convolutional sparse coding. In this paper we propose a computationally efficient algorithm, that apply the convolution in the spatial domain, to solve the CSC problem when the corresponding dictionary filters are separable. Our algorithm, based on the ISTA framework, use a two-term penalty function to attain competitive results when compared to the state-of-the-art methods in terms of computational performance, sparsity and reconstruction quality.

关键词： convolutional sparse Representation convolutional sparse coding Separable Filters sparse coding Filter banks Separable Filters Frequency-domain analysis efficient algorithm spatial domain Dictionaries as Topic Cheyne-Stokes Respiration Computational efficiency Linear Combination Convolution representations two-dimensional filter Banks

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