Scalable Online Convolutional Sparse Coding

Wang, Yaqing; Yao, Quanming; Kwok, James T.; Ni, Lei

doi:10.1109/tip.2018.2842152

Cited by 42 publications

(44 citation statements)

References 29 publications

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“…Another aspect of this advantage is our ability to run in an online manner, even for a single input image. This stands in sharp contrast to other recent online methods [30,31] which allow for online training but only in the case of streaming images. Other approaches took a step further and proposed partitioning the image into smaller sub-images [32], but this is still far from [32] and the SBDL algorithm [1].…”

Section: Relation To Other Methodsmentioning

confidence: 67%

A Local Block Coordinate Descent Algorithm for the CSC Model

Zisselman

Sulam²,

Elad

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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The Convolutional Sparse Coding (CSC) model has recently gained considerable traction in the signal and image processing communities. By providing a global, yet tractable, model that operates on the whole image, the CSC was shown to overcome several limitations of the patch-based sparse model while achieving superior performance in various applications. Contemporary methods for pursuit and learning the CSC dictionary often rely on the Alternating Direction Method of Multipliers (ADMM) in the Fourier domain for the computational convenience of convolutions, while ignoring the local characterizations of the image. A recent work by Papyan et al. [1] suggested the SBDL algorithm for the CSC, while operating locally on image patches. SBDL demonstrates better performance compared to the Fourier-based methods, albeit still relying on the ADMM.In this work we maintain the localized strategy of the SBDL, while proposing a new and much simpler approach based on the Block Coordinate Descent algorithm -this method is termed Local Block Coordinate Descent (LoBCoD). Furthermore, we introduce a novel stochastic gradient descent version of LoBCoD for training the convolutional filters. The Stochastic-LoBCoD leverages the benefits of online learning, while being applicable to a single training image. We demonstrate the advantages of the proposed algorithms for image inpainting and multi-focus image fusion, achieving state-of-the-art results.

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Section: Relation To Other Methodsmentioning

confidence: 67%

A Local Block Coordinate Descent Algorithm for the CSC Model

Zisselman

Sulam²,

Elad

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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“…Let η This can be solved by closed form solution. Using the reordering trick used in [21], [37], we first put all d k 's in the columns ofD = [d 1 , . .…”

Section: A Filter Updatementioning

confidence: 99%

Generalized Convolutional Sparse Coding With Unknown Noise

Wang

Kwok

2020

IEEE Trans. on Image Process.

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Convolutional sparse coding (CSC) can learn representative shift-invariant patterns from multiple kinds of data. However, existing CSC methods can only model noises from Gaussian distribution, which is restrictive and unrealistic. In this paper, we propose a general CSC model capable of dealing with complicated unknown noise. The noise is now modeled by Gaussian mixture model, which can approximate any continuous probability density function. We use the expectation-maximization algorithm to solve the problem and design an efficient method for the weighted CSC problem in maximization step. The crux is to speed up the convolution in the frequency domain while keeping the other computation involving weight matrix in the spatial domain. Besides, we simultaneously update the dictionary and codes by nonconvex accelerated proximal gradient algorithm without bringing in extra alternating loops. The resultant method obtains comparable time and space complexity compared with existing CSC methods. Extensive experiments on synthetic and real noisy biomedical data sets validate that our method can model noise effectively and obtain high-quality filters and representation.

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“…Moreover, inferring z i from x is an iterative process that may require a large number of iterations, thus, less suitable for real-time tasks. Work on speeding up the ADMM method for CD learning is done in [13]. In [14], a consensus-based optimization framework has been proposed that makes CSC tractable on large-scale datasets.…”

Section: Convolutional Sparse Codingmentioning

confidence: 99%

Learned Convolutional Sparse Coding

Sreter

Giryes

2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

101

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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 method 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 run-time.

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Scalable Online Convolutional Sparse Coding

Cited by 42 publications

References 29 publications

A Local Block Coordinate Descent Algorithm for the CSC Model

A Local Block Coordinate Descent Algorithm for the CSC Model

Generalized Convolutional Sparse Coding With Unknown Noise

Learned Convolutional Sparse Coding

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