JPEG Artifacts Reduction via Deep Convolutional Sparse Coding

To effectively reduce JPEG compression artifacts, we propose a deep convolutional sparse coding (DCSC) network architecture. We design our DCSC in the framework of classic learned iterative shrinkage-threshold algorithm. To focus on recognizing and separating artifacts only, we sparsely code the fea...

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Published in:Proceedings / IEEE International Conference on Computer Vision pp. 2501 - 2510
Main Authors: Fu, Xueyang, Zha, Zheng-Jun, Wu, Feng, Ding, Xinghao, Paisley, John
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2019
Subjects:
Convolution
Convolutional codes
Encoding
Feature extraction
Image coding
Task analysis
Transform coding
ISSN:2380-7504
Online Access:Get full text
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Abstract To effectively reduce JPEG compression artifacts, we propose a deep convolutional sparse coding (DCSC) network architecture. We design our DCSC in the framework of classic learned iterative shrinkage-threshold algorithm. To focus on recognizing and separating artifacts only, we sparsely code the feature maps instead of the raw image. The final de-blocked image is directly reconstructed from the coded features. We use dilated convolution to extract multi-scale image features, which allows our single model to simultaneously handle multiple JPEG compression levels. Since our method integrates model-based convolutional sparse coding with a learning-based deep neural network, the entire network structure is compact and more explainable. The resulting lightweight model generates comparable or better de-blocking results when compared with state-of-the-art methods.
AbstractList To effectively reduce JPEG compression artifacts, we propose a deep convolutional sparse coding (DCSC) network architecture. We design our DCSC in the framework of classic learned iterative shrinkage-threshold algorithm. To focus on recognizing and separating artifacts only, we sparsely code the feature maps instead of the raw image. The final de-blocked image is directly reconstructed from the coded features. We use dilated convolution to extract multi-scale image features, which allows our single model to simultaneously handle multiple JPEG compression levels. Since our method integrates model-based convolutional sparse coding with a learning-based deep neural network, the entire network structure is compact and more explainable. The resulting lightweight model generates comparable or better de-blocking results when compared with state-of-the-art methods.
Author Wu, Feng
Fu, Xueyang
Zha, Zheng-Jun
Paisley, John
Ding, Xinghao
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  organization: Columbia University
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Snippet To effectively reduce JPEG compression artifacts, we propose a deep convolutional sparse coding (DCSC) network architecture. We design our DCSC in the...
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SubjectTerms Convolution
Convolutional codes
Encoding
Feature extraction
Image coding
Task analysis
Transform coding
Title JPEG Artifacts Reduction via Deep Convolutional Sparse Coding
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