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• W3200361172 abstract "In order to enhance compressed JPEG image, a deep convolutional sparse coding network is proposed in this article. The network integrates state-of-the-art dynamic convolution to extract multi-scale image features, and uses convolutional sparse coding to separate image artifacts to generate coded feature for the final image reconstruction. Since this architecture consolidates model-based convolutional sparse coding with deep neural network, that allow this method has more interpretability. Also, compared with the existing network, which uses a dilated convolution as a feature extraction approach, this proposed concatenated dynamic method has improved de-blocking result in both numerical experiments and visual effect. Besides, in the higher compressed quality task, the proposed model has more pronounced improvement in reconstructed image quality evaluations." @default.
• W3200361172 created "2021-09-27" @default.
• W3200361172 creator A5083484694 @default.
• W3200361172 creator A5089801363 @default.
• W3200361172 date "2021-01-01" @default.
• W3200361172 modified "2023-09-23" @default.
• W3200361172 title "The Concatenated Dynamic Convolutional and Sparse Coding on Image Artifacts Reduction" @default.
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• W3200361172 doi "https://doi.org/10.1007/978-3-030-86960-1_8" @default.
• W3200361172 hasPublicationYear "2021" @default.
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