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| Image Super-Resolution Reconstruction Based on l1 Induced Lightweight Deep Networks |
| ZHANG Dabao1, ZHAO Jianwei1,2, ZHOU Zhenghua1 |
1. College of Sciences, China Jiliang University, Hangzhou 310018;
2. Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Hangzhou 310018 |
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Abstract Existing deep-learning based super-resolution reconstruction methods improve the reconstruction performance of networks by deepening networks. However, sharp increase of the number of network weights is caused by deepening networks, resulting in a huge burden for the storage and training network. With the consideration of the sparsity of noise, the cost of training network and the sharpness of reconstructed edges, an image super-resolution reconstruction is proposed based on l1 induced lightweight deep networks integrating with the idea of model-driven and data-driven. Firstly, the split Bregman algorithm and soft threshold operator are utilized to deduce an effective iterative algorithm from the l1 reconstruction optimization model with an edge regularization term. Secondly, a corresponding recursive deep network is designed for image reconstruction under the guidance of the iterative algorithm. Therefore, the proposed deep network is designed under the guidance of the reconstruction optimization model, and its derived recursive structure reduces the number of network weights due to its property of weight sharing. Experimental results show that the proposed method achieves good reconstruction performance with less number of network weights.
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Received: 08 October 2022
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| Fund:Natural Science Foundation of Zhejiang Province(No.LY22F020002,LSY19F020001) |
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Corresponding Authors:
ZHAO Jianwei, Ph.D., professor. Her research interests include deep learning and image processing.
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| About author:: ZHANG Dabao, master student. His research interests include deep learning and image processing.ZHOU Zhenghua, Ph.D., associate professor. His research interests include deep learning and image processing. |
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2022, Vol. 35 
