Wavelet attention-based implicit multi-granularity super-resolution network
Abstract Image super-resolution (SR) is a fundamental challenge in the field of computer vision. Recently, Convolutional Neural Network (CNN)-based methods for image SR have achieved significant progress across various SR tasks. However, most current research focuses on designing deeper and wider ar...
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| Format: | Article |
| Language: | English |
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Springer
2025-04-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-025-01862-4 |
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| author | Chen Boying Shi Jie |
| author_facet | Chen Boying Shi Jie |
| author_sort | Chen Boying |
| collection | DOAJ |
| description | Abstract Image super-resolution (SR) is a fundamental challenge in the field of computer vision. Recently, Convolutional Neural Network (CNN)-based methods for image SR have achieved significant progress across various SR tasks. However, most current research focuses on designing deeper and wider architectures, often sacrificing computational burden and speed in order to improve image SR quality. To achieve more efficient SR methods, this paper proposes a Wavelet Attention Network (WANet) for image SR. Firstly, a wavelet-based attention module is proposed. Compared to existing self-attention modules, the wavelet attention module decomposes image features into different frequency components using wavelet transforms. It then applies a self-attention mechanism to capture multi-scale features, enabling a more efficient and larger receptive field to help the network capture long-range feature dependencies. Secondly, local implicit features are introduced to enhance the encoder’s ability to aggregate local neighborhood features. Finally, coarse and fine-grained interwoven pixel features are collaboratively associated to improve the performance of the implicit feature decoder. Experimental comparisons with state-of-the-art SR methods demonstrate the effectiveness and superiority of WANet in the field of image SR. |
| format | Article |
| id | doaj-art-b78a5eb2df4346df8488518f29047f50 |
| institution | DOAJ |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-b78a5eb2df4346df8488518f29047f502025-08-20T02:55:36ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-04-0111511110.1007/s40747-025-01862-4Wavelet attention-based implicit multi-granularity super-resolution networkChen Boying0Shi Jie1School of Electronics and Information Engineering, Suzhou University of Science and TechnologySchool of Electronics and Information Engineering, Suzhou University of Science and TechnologyAbstract Image super-resolution (SR) is a fundamental challenge in the field of computer vision. Recently, Convolutional Neural Network (CNN)-based methods for image SR have achieved significant progress across various SR tasks. However, most current research focuses on designing deeper and wider architectures, often sacrificing computational burden and speed in order to improve image SR quality. To achieve more efficient SR methods, this paper proposes a Wavelet Attention Network (WANet) for image SR. Firstly, a wavelet-based attention module is proposed. Compared to existing self-attention modules, the wavelet attention module decomposes image features into different frequency components using wavelet transforms. It then applies a self-attention mechanism to capture multi-scale features, enabling a more efficient and larger receptive field to help the network capture long-range feature dependencies. Secondly, local implicit features are introduced to enhance the encoder’s ability to aggregate local neighborhood features. Finally, coarse and fine-grained interwoven pixel features are collaboratively associated to improve the performance of the implicit feature decoder. Experimental comparisons with state-of-the-art SR methods demonstrate the effectiveness and superiority of WANet in the field of image SR.https://doi.org/10.1007/s40747-025-01862-4Continuous super-resolution reconstructionImplicit neural representationWavelet transformAttention mechanism |
| spellingShingle | Chen Boying Shi Jie Wavelet attention-based implicit multi-granularity super-resolution network Complex & Intelligent Systems Continuous super-resolution reconstruction Implicit neural representation Wavelet transform Attention mechanism |
| title | Wavelet attention-based implicit multi-granularity super-resolution network |
| title_full | Wavelet attention-based implicit multi-granularity super-resolution network |
| title_fullStr | Wavelet attention-based implicit multi-granularity super-resolution network |
| title_full_unstemmed | Wavelet attention-based implicit multi-granularity super-resolution network |
| title_short | Wavelet attention-based implicit multi-granularity super-resolution network |
| title_sort | wavelet attention based implicit multi granularity super resolution network |
| topic | Continuous super-resolution reconstruction Implicit neural representation Wavelet transform Attention mechanism |
| url | https://doi.org/10.1007/s40747-025-01862-4 |
| work_keys_str_mv | AT chenboying waveletattentionbasedimplicitmultigranularitysuperresolutionnetwork AT shijie waveletattentionbasedimplicitmultigranularitysuperresolutionnetwork |