A direction-decoupled non-local attention network for single image super-resolution
The non-local attention mechanism has often been exploited in deep learning to capture long-range dependencies (LRDs) from the same image for enhancing the performance of various image processing methods. However, the initially proposed non-local attention process inevitably yields extremely-high co...
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| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/164656 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | The non-local attention mechanism has often been exploited in deep learning to capture long-range dependencies (LRDs) from the same image for enhancing the performance of various image processing methods. However, the initially proposed non-local attention process inevitably yields extremely-high computation complexity, since all the feature points are involved in computing the LRDs. To address this concern, a recently proposed criss-cross network (CCNet), which has a recurrent criss-cross attention (RCCA) module, is used to compute the LRDs by involving only a small set of feature points for significantly reducing computation. Motivated by the RCCA, a novel direction-decoupled non-local attention (DNA) module is proposed in this paper that is able to further reduce the computation complexity of RCCA by half approximately. To verify the performance of our new non-local attention module, a DNA network is developed for conducting single image super-resolution (SISR). Extensive experimental results have clearly demonstrated the superiority of using our DNA network for SISR when compared with that of state-of-the-art methods. |
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