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| Format: | Preprint |
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2023
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| Online-Zugang: | https://arxiv.org/abs/2309.16494 |
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| _version_ | 1866916932274356224 |
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| author | He, Zewei Chen, Zixuan Li, Jinlei Lu, Ziqian Sun, Xuecheng Luo, Hao Lu, Zhe-Ming Markakis, Evangelos K. |
| author_facet | He, Zewei Chen, Zixuan Li, Jinlei Lu, Ziqian Sun, Xuecheng Luo, Hao Lu, Zhe-Ming Markakis, Evangelos K. |
| contents | Recently, deep learning-based methods have dominated image dehazing domain. A multi-receptive-field non-local network (MRFNLN) consisting of the multi-stream feature attention block (MSFAB) and the cross non-local block (CNLB) is presented in this paper to further enhance the performance. We start with extracting richer features for dehazing. Specifically, a multi-stream feature extraction (MSFE) sub-block, which contains three parallel convolutions with different receptive fields (i.e., $1\times 1$, $3\times 3$, $5\times 5$), is designed for extracting multi-scale features. Following MSFE, an attention sub-block is employed to make the model adaptively focus on important channels/regions. These two sub-blocks constitute our MSFAB. Then, we design a cross non-local block (CNLB), which can capture long-range dependencies beyond the query. Instead of the same input source of query branch, the key and value branches are enhanced by fusing more preceding features. CNLB is computation-friendly by leveraging a spatial pyramid down-sampling (SPDS) strategy to reduce the computation and memory consumption without sacrificing the performance. Last but not least, a novel detail-focused contrastive regularization (DFCR) is presented by emphasizing the low-level details and ignoring the high-level semantic information in a representation space specially designed for dehazing. Comprehensive experimental results demonstrate that the proposed MRFNLN model outperforms recent state-of-the-art dehazing methods with less than 1.5 Million parameters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_16494 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Accurate and lightweight dehazing via multi-receptive-field non-local network and novel contrastive regularization He, Zewei Chen, Zixuan Li, Jinlei Lu, Ziqian Sun, Xuecheng Luo, Hao Lu, Zhe-Ming Markakis, Evangelos K. Computer Vision and Pattern Recognition Recently, deep learning-based methods have dominated image dehazing domain. A multi-receptive-field non-local network (MRFNLN) consisting of the multi-stream feature attention block (MSFAB) and the cross non-local block (CNLB) is presented in this paper to further enhance the performance. We start with extracting richer features for dehazing. Specifically, a multi-stream feature extraction (MSFE) sub-block, which contains three parallel convolutions with different receptive fields (i.e., $1\times 1$, $3\times 3$, $5\times 5$), is designed for extracting multi-scale features. Following MSFE, an attention sub-block is employed to make the model adaptively focus on important channels/regions. These two sub-blocks constitute our MSFAB. Then, we design a cross non-local block (CNLB), which can capture long-range dependencies beyond the query. Instead of the same input source of query branch, the key and value branches are enhanced by fusing more preceding features. CNLB is computation-friendly by leveraging a spatial pyramid down-sampling (SPDS) strategy to reduce the computation and memory consumption without sacrificing the performance. Last but not least, a novel detail-focused contrastive regularization (DFCR) is presented by emphasizing the low-level details and ignoring the high-level semantic information in a representation space specially designed for dehazing. Comprehensive experimental results demonstrate that the proposed MRFNLN model outperforms recent state-of-the-art dehazing methods with less than 1.5 Million parameters. |
| title | Accurate and lightweight dehazing via multi-receptive-field non-local network and novel contrastive regularization |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2309.16494 |