| TIAN Shu (田 澍),ZHOU Hongyang.[J].高技术通讯(英文),2024,30(2):117~127 |
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| Faster split-based feedback network for image super-resolution |
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| DOI：10. 3772 / j. issn. 1006-6748. 2024. 02. 002 |
| 中文关键词: |
| 英文关键词: super-resolution (SR), split-based feedback, contrastive learning |
| 基金项目: |
| Author Name | Affiliation | | TIAN Shu (田 澍) | (School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, P. R. China) | | ZHOU Hongyang | |
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| 中文摘要: |
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| 英文摘要: |
| Although most of the existing image super-resolution (SR) methods have achieved superior performance,contrastive learning for high-level tasks has not been fully utilized in the existing image SR methods based on deep learning. This work focuses on two well-known strategies developed for lightweight and robust SR, i. e. , contrastive learning and feedback mechanism, and proposes an integrated solution called a split-based feedback network (SPFBN). The proposed SPFBN is based on a feedback mechanism to learn abstract representations and uses contrastive learning to explore high information in the representation space. Specifically, this work first uses hidden states and constraints in recurrent neural network (RNN) to implement a feedback mechanism. Then, use contrastive learning to perform representation learning to obtain high-level information by pushing the final image to the intermediate images and pulling the final SR image to the high-resolution image.Besides, a split-based feedback block (SPFB) is proposed to reduce model redundancy, which tolerates features with similar patterns but requires fewer parameters. Extensive experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-art methods.Moreover, this work extends the experiment to prove the effectiveness of this method and shows better overall reconstruction quality. |
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