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28 March 2024 |
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Article overview
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Magic ELF: Image Deraining Meets Association Learning and Transformer | Kui Jiang
; Zhongyuan Wang
; Chen Chen
; Zheng Wang
; Laizhong Cui
; Chia-Wen Lin
; | Date: |
21 Jul 2022 | Abstract: | Convolutional neural network (CNN) and Transformer have achieved great
success in multimedia applications. However, little effort has been made to
effectively and efficiently harmonize these two architectures to satisfy image
deraining. This paper aims to unify these two architectures to take advantage
of their learning merits for image deraining. In particular, the local
connectivity and translation equivariance of CNN and the global aggregation
ability of self-attention (SA) in Transformer are fully exploited for specific
local context and global structure representations. Based on the observation
that rain distribution reveals the degradation location and degree, we
introduce degradation prior to help background recovery and accordingly present
the association refinement deraining scheme. A novel multi-input attention
module (MAM) is proposed to associate rain perturbation removal and background
recovery. Moreover, we equip our model with effective depth-wise separable
convolutions to learn the specific feature representations and trade off
computational complexity. Extensive experiments show that our proposed method
(dubbed as ELF) outperforms the state-of-the-art approach (MPRNet) by 0.25 dB
on average, but only accounts for 11.7\% and 42.1\% of its computational cost
and parameters. The source code is available at
this https URL | Source: | arXiv, 2207.10455 | Services: | Forum | Review | PDF | Favorites |
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