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检索条件"主题词=convolutional sparse coding"

共 168 条记录，以下是71-80 订阅

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LEARNED convolutional sparse coding

LEARNED CONVOLUTIONAL SPARSE CODING

引用

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Sreter, Hillel Giryes, Raja Tel Aviv Univ Sch Elect Engn Tel Aviv Israel

ISBN: (纸本)9781538646588

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

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COUPLED FEATURE LEARNING VIA STRUCTURED convolutional sparse coding FOR MULTIMODAL IMAGE FUSION 47

COUPLED FEATURE LEARNING VIA STRUCTURED CONVOLUTIONAL SPARSE...

引用

47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Veshki, Farshad G. Vorobyov, Sergiy A. Aalto Univ Dept Signal Proc & Acoust Espoo Finland

ISBN: (纸本)9781665405409

A novel method for learning correlated features in multimodal images based on convolutional sparse coding with applications to image fusion is presented. In particular, the correlated features are captured as coupled filters in convolutional dictionaries. At the same time, the shared and independent features are approximated using separate convolutional sparse codes and a common dictionary. The resulting optimization problem is addressed using alternating direction method of multipliers. The coupled filters are fused based on a maximum-variance rule, and a maximum-absolute-value rule is used to fuse the sparse codes. The proposed method does not entail any prelearning stage. The experimental evaluations using medical and infrared-visible image datasets demonstrate the superiority of our method compared to state-of-the-art algorithms in terms of preserving the details and local intensities as well as improving objective metrics.

关键词： Multimodal image fusion convolutional sparse coding structured dictionary learning

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Image Denoising Using convolutional sparse coding Network with Dry Friction 16th

Image Denoising Using Convolutional Sparse Coding Network wi...

引用

16th Asian Conference on Computer Vision (ACCV)

作者： Zhang, Yali Wang, Xiaofan Wang, Fengpin Wang, Jinjia Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Peoples R China Yanshan Univ Hebei Key Lab Informat Transmiss & Signal Proc Qinhuangdao 066004 Peoples R China

ISBN: (纸本)9783031263187;9783031263194

convolutional sparse coding model has been successfully used in some tasks such as signal or image processing and classification. The recently proposed supervised convolutional sparse coding network (CSCNet) model based on the Minimum Mean Square Error (MMSE) approximation shows the similar PSNR value for image denoising problem with state of the art methods while using much fewer parameters. The CSCNet uses the learning convolutional iterative shrinkage-thresholding algorithms (LISTA) based on the convolutional dictionary setting. However, LISTA methods are known to converge to local minima. In this paper we proposed one novel algorithm based on LISTA with dry friction, named LISTDFA. The dry friction enters the LISTDFA algorithm through proximal mapping. Due to the nature of dry friction, the LISTDFA algorithm is proven to converge in a finite time. The corresponding iterative neural network preserves the computational simplicity of the original CSCNet, and can reach a better local minima practically.

关键词： Image denoising convolutional sparse coding Iterative shrinkage thresholding algorithms Dry friction

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IMAGE DE-RAINING VIA RDL: WHEN REWEIGHTED convolutional sparse coding MEETS DEEP LEARNING

IMAGE DE-RAINING VIA RDL: WHEN REWEIGHTED CONVOLUTIONAL SPAR...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： He, Jingwei Yu, Lei Yang, Wen Wuhan Univ Sch Elect & Informat Wuhan 430072 Peoples R China

ISBN: (纸本)9781509066315

Over the past few decades, image de-raining has witnessed substantial progress due to the development of priors and deep learning based methods. However, few studies combine the merits of both. In this paper, we argue that domain expertise of conventional convolutional sparse coding (CSC) is still valuable, and it can be combined with the key ingredients of deep learning to achieve further improved results. Specifically, motivated by the success of reweighting algorithms, we propose solving the CSC model by learning weighted iterative soft thresholding algorithm (LwISTA) in a convolutional manner where the reweighted l(1)-norm is introduced. Based on this, we present a novel framework for single image de-raining, in which the channel attention is employed to learn the weight. Extensive experiments demonstrate the superiority of our method over recent state-of-the-art image de-raining methods, in terms of both quantitative and qualitative results.

关键词： Image de-raining convolutional sparse coding reweighted l(1)-minimization deep learning

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HIGH ACCURACY COMPRESSIVE CHROMO-TOMOGRAPHY RECONSTRUCTION VIA convolutional sparse coding

HIGH ACCURACY COMPRESSIVE CHROMO-TOMOGRAPHY RECONSTRUCTION V...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Li, Baoping Zhang, Xuesong Jiang, Jing Chen, Yuzhong Zhang, Qi Ming, Anlong Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China Beijing Union Univ Dept Commun Engn Beijing 100101 Peoples R China

ISBN: (纸本)9781728113319

Over the last decade various compressive snapshot hyperspectral imaging methods have been proposed. The limited reconstruction quality from severely compressed measurements, however, has been a practical barrier to real applications. This paper proposes a compressive chromo-tomography framework that incorporates the convolutional sparse coding (CSC) prior into the classical total variation and L1 regularization functionals. Such a combination allows excellent high-frequency recovery capabilities of CSC, while effectively suppressing ghost artifacts in tomographic reconstructions. Since nondifferentiable regularizers are employed, we propose a preconditioned alternating direction method of multipliers (ADMM) for flexible and efficient solutions, both for the reconstruction task and for hyperspectral convolutional dictionary learning. We demonstrate in our numerical experiments that just 25 learned 3D CSC filters can fulfill a rather effective hyperspectral imagery representation and that the proposed method is capable of high accuracy reconstructions.

关键词： Hyperspectral image reconstruction convolutional sparse coding alternating direction method of multipliers (ADMM) preconditioning

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EFFICIENT convolutional sparse coding

EFFICIENT CONVOLUTIONAL SPARSE CODING

引用

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Wohlberg, Brendt Los Alamos Natl Lab Div Theoret Los Alamos NM 87545 USA

ISBN: (纸本)9781479928934

When applying sparse representation techniques to images, the standard approach is to independently compute the representations for a set of overlapping image patches. This method performs very well in a variety of applications, but the independent sparse coding of each patch results in a representation that is not optimal for the image as a whole. A recent development is convolutional sparse coding, in which a sparse representation for an entire image is computed by replacing the linear combination of a set of dictionary vectors by the sum of a set of convolutions with dictionary filters. A disadvantage of this formulation is its computational expense, but the development of efficient algorithms has received some attention in the literature, with the current leading method exploiting a Fourier domain approach. The present paper introduces a new way of solving the problem in the Fourier domain, leading to substantially reduced computational cost.

关键词： sparse Representation sparse coding convolutional sparse coding ADMM

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MATCHING PURSUIT BASED convolutional sparse coding

MATCHING PURSUIT BASED CONVOLUTIONAL SPARSE CODING

引用

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Plaut, Elad Giryes, Raja Tel Aviv Univ Sch Elect Engn Tel Aviv Israel

ISBN: (纸本)9781538646588

convolutional sparse coding using the l(0,infinity )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,infinity) 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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sparse-VIEW CT RECONSTRUCTION VIA convolutional sparse coding 16

SPARSE-VIEW CT RECONSTRUCTION VIA CONVOLUTIONAL SPARSE CODIN...

引用

16th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Bao, Peng Xia, Wenjun Yang, Kang Zhou, Jiliu Zhang, Yi Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China

ISBN: (纸本)9781538636411

Traditional dictionary learning based CT reconstruction methods are patch-based and the features learned with these methods often contain shifted versions of the same features. To deal with these problems, the convolutional sparse coding (CSC) has been proposed and introduced into various applications. In this paper, inspired by the successful applications of CSC in the field of signal processing, we propose a novel sparse-view CT reconstruction method based on CSC with gradient regularization on feature maps. By directly working on the whole image, which need not to divide the image into overlapped patches like dictionary learning based methods, the proposed method can maintain more details and avoid the artifacts caused by patch aggregation. Experimental results demonstrate that the proposed method has better performance than several existing algorithms in both qualitative and quantitative aspects.

关键词： sparse-view convolutional sparse coding

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A SELF-SUPERVISED HYPERSPECTRAL IMAGE RESTORATION METHOD BASED ON convolutional sparse coding AND SUPERPIXEL SEGMENTATION

A SELF-SUPERVISED HYPERSPECTRAL IMAGE RESTORATION METHOD BAS...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Jiang, Zhongshun Zhu, Honglin Qiu, Yi Qian, Yuntao Zhejiang Univ Coll Comp Sci Hangzhou Peoples R China Huzhou Univ Coll Engn Huzhou Peoples R China

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

Denoising is a common preprocessing step before hyperspectral image (HSI) analysis and interpretation. Since noisefree training samples are not required, self-supervised deep learning is becoming a new research trend in image restoration. In this paper, we propose a self-supervised denoising method based on noise2noise learning scheme that assumes two noisy signals in a pair being randomly generated from the same clean signal. To obtain noisy-noisy training pairs that meet the requirement of noise2noise learning, the target noisy HSI is divided into several homogeneous image patches by superpixel segmentation. Pixels located in the same superpixel have strong spectral similarity and spatial neighborhood, so they are used to construct noisy-noisy training pairs and the spectral-spatial correlations in an HSI can be embedded in our denoiser via training samples. Moreover, the convolutional sparse coding is introduced and unfolded as the backbone network for spectral signature representation and restoration. Experimental results show that our method is competitive compared with the state-of-the-art methods.

关键词： Hyperspectral images denoising selfsupervised learning convolutional sparse coding unfolded network

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Adaptive Coded Aperture Design by Motion Estimation using convolutional sparse coding in Compressive Spectral Video Sensing 8

Adaptive Coded Aperture Design by Motion Estimation using Co...

引用

8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

作者： Diaz, N. Noriega-Wandurraga, C. Basarab, A. Tourneret, J-Y Arguelle, H. Univ Ind Santander UIS Dept Elect Engn Bucaramanga 680002 Colombia UIS Dept Math Bucaramanga 680002 Colombia Univ Toulouse IRIT CNRS UMR 5505 F-31062 Toulouse France Univ Toulouse IRIT INP ENSEEIHT TeSA F-31071 Toulouse France UIS Dept Comp Sci Bucaramanga 680002 Colombia

ISBN: (纸本)9781728155494

This paper proposes a new motion estimation method based on convolutional sparse coding to adaptively design the colored-coded apertures in static and dynamic spectral videos. The motion in a spectral video is estimated from a low-resolution reconstruction of the datacube by training a convolutional dictionary per spectral band and solving a minimization problem. Simulations show improvements in terms of peak signal-to-noise ratio (of up to 2 dB) of the reconstructed videos by using the proposed approach, compared with state-of-art non-adaptive coded apertures.

关键词： Compressive spectral video motion estimation adaptive imaging convolutional sparse coding

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