Open AccessLightweight single image deraining algorithm incorporating visual saliency
Author(s) -
Hu Mingdi,
Yang Jingbing,
Ling Nam,
Liu Yuhong,
Fan Jiulun
Publication year - 2022
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12550
Subject(s) - computer science , convolution (computer science) , block (permutation group theory) , dilation (metric space) , feature (linguistics) , artificial intelligence , image (mathematics) , pattern recognition (psychology) , streak , algorithm , computer vision , convolutional neural network , artificial neural network , mathematics , linguistics , philosophy , physics , geometry , combinatorics , optics
There are still some challenges in the task of single image rain removal, such as artefact remnant, background over‐smooth, and increasingly complex and heavy‐weight network architecture. Especially too heavy‐weight network to fit outdoor detection devices or mobile devices. To address the above challenges, we propose a lightweight single image Deraining algorithm incorporating visual attention saliency mechanisms (LDVS). The proposed network consists of five blocks and two convolution operations, where each block consists of a dilation convolution module and a convolutional block attention module (CBAM). Specifically, visual saliency module CBAM is used for accurate localization of rain streak, and further the combinations of dilated convolution with CBAM is used to extract feature maps of rain streaks faithfully, which is able to remove artefact remnant while maintaining background details. A good tradeoff is presented between the network's weight size and effect of rain removal. Specifically, with only 48,268 parameters, the proposed model can achieve a guaranteed performance. Extensive experiments on a few typical rainy scenarios on synthetic and real‐world datasets have demonstrated that to achieve the same level of performance, the proposed method has far smaller size than most of the baselines under both qualitative and quantitative analyses.