mlsanityData to 14 Oct 2026

DPAFNet:Dual Path Attention Fusion Network for Single Image Deraining

arXiv 2401.08185 ↗First posted 16 Jan 2024cs.CV cs.AI eess.IVcited by 0

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Abstract (arXiv)

Rainy weather will have a significant impact on the regular operation of the imaging system. Based on this premise, image rain removal has always been a popular branch of low-level visual tasks, especially methods using deep neural networks. However, most neural networks are but-branched, such as only using convolutional neural networks or Transformers, which is unfavourable for the multidimensional fusion of image features. In order to solve this problem, this paper proposes a dual-branch attention fusion network. Firstly, a two-branch network structure is proposed. Secondly, an attention fusion module is proposed to selectively fuse the features extracted by the two branches rather than simply adding them. Finally, complete ablation experiments and sufficient comparison experiments prove the rationality and effectiveness of the proposed method.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 31 references that are in the map. First posted on arXiv 16 Jan 2024.

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23 of 31 references.

References · 23

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11 more
  1. 2018Toward Convolutional Blind Denoising of Real Photographscited by 274
  2. 2019Progressive Image Deraining Networks: A Better and Simpler Baselinecited by 272
  3. 2020Multi-Scale Progressive Fusion Network for Single Image Derainingcited by 260
  4. 2018Density-aware Single Image De-raining using a Multi-stream Dense Networkcited by 248
  5. 2018Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Derainingcited by 205
  6. 2017DesnowNet: Context-Aware Deep Network for Snow Removalcited by 200
  7. 2017Image De-raining Using a Conditional Generative Adversarial Networkcited by 197
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  10. 2019Uncertainty Guided Multi-Scale Residual Learning-using a Cycle Spinning CNN for Single Image De-Rainingcited by 117
  11. 2017AlignedReID: Surpassing Human-Level Performance in Person Re-Identificationcited by 61

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