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Edge-preserving image deraining network using cumulative feature aggregation
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AI
DOI：10.1016/j.asoc.2024.111887.png)
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Abstract
En 中文
This study proposes an edge-preserving image deraining network using a wavelet feature aggregation method. Wavelet subbands re correlated with each other, and the high-frequency subband in the horizontal direction is the least affected by rain streak contamination. On this basis, we introduce a single image deraining network that cumulatively aggregates wavelet subband features according to their importance. The network architecture primarily comprises a wavelet feature aggregation block and a residue channel guide block. The aggregation of features with the cumulative wavelet feature aggregation block moves downward and upward, and a long shortterm memory-based multiscale attentive rain streak removal block is developed to serve as the backbone for rain streak removal. We use a residual channel map based on the low-frequency subband to construct guide features that assist in rain streak removal. A repetitive image restoration framework that incorporates two proposed blocks is used to iteratively improve rainy images. We test the proposed network on various image datasets and compare the deraining performance with those of existing methods The experimental results demonstrate that the performance of the proposed scheme is superior to that of other tested deraining methods. https://github. com/SYChoi98/CWFANet/
Keywords:
Single image deraining
Wavelet feature aggregation
Residue channel
Fusion
Long short -term memory
Multiscale attention
Stationary wavelet transform
Journal
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