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UC-former: A multi-scale image deraining network using enhanced transformer
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NETL
NSTL
Elsevier
While convolutional neural networks (CNN) have achieved remarkable performance in single image deraining tasks， it is still a very challenging task due to CNN's limited receptive field and the unreality of the output image。 In this paper， UC-former， an effective and efficient U-shaped architecture based on transformer for image deraining was presented。 In UC-former， there are two core designs to avoid heavy self-attention computation and inefficient communications across encoder and decoder。 First， we propose a novel channel across Transformer block， which computes self-attention between channels。 It significantly reduces the computational complexity of high-resolution rain maps while capturing global context。 Second， we propose a multi-scale feature fusion module between the encoder and decoder to combine low-level local features and high-level non-local features。 In addition， we employ depth-wise convolution and H-Swish non-linear activation function in Transformer Blocks to enhance rain removal authenticity。 Extensive experiments indicate that our method outperforms the state-of-the-art deraining approaches on synthetic and real-world rainy datasets。
Single image derainingMulti-scale feature fusionTransformerSelf-attention
Weina Zhou、Linhui Ye
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Shanghai Maritime University, Shanghai 201306, China
NETL
NSTL
Elsevier
