Latent Degradation Representation Constraint for Single Image Deraining
- URL: http://arxiv.org/abs/2309.04780v3
- Date: Thu, 18 Jan 2024 07:13:40 GMT
- Title: Latent Degradation Representation Constraint for Single Image Deraining
- Authors: Yuhong He, Long Peng, Lu Wang, Jun Cheng
- Abstract summary: We propose a novel Latent Degradation Representation Constraint Network (LDRCNet) that consists of Direction-Aware (DAEncoder), UNet Deraining Network, and MSIBlock.
Experimental results on synthetic and real datasets demonstrate that our method achieves new state-of-the-art performance.
- Score: 13.414207526373959
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Since rain streaks show a variety of shapes and directions, learning the
degradation representation is extremely challenging for single image deraining.
Existing methods are mainly targeted at designing complicated modules to
implicitly learn latent degradation representation from coupled rainy images.
This way, it is hard to decouple the content-independent degradation
representation due to the lack of explicit constraint, resulting in over- or
under-enhancement problems. To tackle this issue, we propose a novel Latent
Degradation Representation Constraint Network (LDRCNet) that consists of
Direction-Aware Encoder (DAEncoder), UNet Deraining Network, and Multi-Scale
Interaction Block (MSIBlock). Specifically, the DAEncoder is proposed to
adaptively extract latent degradation representation by using the deformable
convolutions to exploit the direction consistency of rain streaks. Next, a
constraint loss is introduced to explicitly constraint the degradation
representation learning during training. Last, we propose an MSIBlock to fuse
with the learned degradation representation and decoder features of the
deraining network for adaptive information interaction, which enables the
deraining network to remove various complicated rainy patterns and reconstruct
image details. Experimental results on synthetic and real datasets demonstrate
that our method achieves new state-of-the-art performance.
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