Jawali, D and Pokala, PK and Seelamantula, CS (2020) Cornet: Composite-Regularized Neural Network for Convolutional Sparse Coding. In: Proceedings - International Conference on Image Processing, 25-28, September 2020, Virtual, Abu Dhabi; United Arab Emirates, pp. 818-822.
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Abstract
Sparse recovery via composite regularization is an interesting approach proposed recently in the literature. One could design nonconvex regularizers through a convex combination of sparsitypromoting penalties with known proximal operators. We develop a new algorithm, namely, convolutional proximal-averaged thresholding algorithm (C-PATA) for composite-regularized convolutional sparse coding (CR-CSC) based on the recently proposed idea of proximal averaging. We develop an autoencoder structure based on the deep-unfolding of C-PATA iterations into neural network layers, which results in the composite-regularized neural network (CoRNet) architecture. The convolutional learned iterative soft-thresholding algorithm becomes a special case of CoRNet. We demonstrate the efficacy of CoRNet considering applications to image denoising and inpainting, and compare the performance with state-of-the-art techniques such as BM3D, convolutional LISTA, and fast and flexible convolutional sparse coding (FFCSC). © 2020 IEEE.
Item Type: | Conference Paper |
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Publication: | Proceedings - International Conference on Image Processing, ICIP |
Publisher: | IEEE Computer Society |
Additional Information: | cited By 0; Conference of 2020 IEEE International Conference on Image Processing, ICIP 2020 ; Conference Date: 25 September 2020 Through 28 September 2020; Conference Code:165772 |
Keywords: | Composite structures; Convolution; Image coding; Image denoising; Iterative methods; Multilayer neural networks; Network coding; Network layers, Convex combinations; Regularized neural networks; Soft-thresholding algorithm; Sparse coding; Sparse recovery; State-of-the-art techniques; Structure-based; Thresholding algorithms, Convolutional neural networks |
Department/Centre: | Division of Electrical Sciences > Electrical Engineering |
Date Deposited: | 22 Jan 2021 10:42 |
Last Modified: | 22 Jan 2021 10:42 |
URI: | http://eprints.iisc.ac.in/id/eprint/67730 |
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