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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| Vydané v: | Proceedings / IEEE International Conference on Computer Vision s. 2501 - 2510 |
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01.10.2019
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| ISSN: | 2380-7504 |
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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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Xueyang surname: Fu fullname: Fu, Xueyang organization: University of Science and Technology of China – sequence: 2 givenname: Zheng-Jun surname: Zha fullname: Zha, Zheng-Jun organization: University of Science and Technology of China – sequence: 3 givenname: Feng surname: Wu fullname: Wu, Feng organization: University of Science and Technology of China – sequence: 4 givenname: Xinghao surname: Ding fullname: Ding, Xinghao organization: Xiamen University – sequence: 5 givenname: John surname: Paisley fullname: Paisley, John 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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