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A deraining network may be interpreted as a condition generator.
Rain streak removal in a single image is a very challenging task due to its ill-posed nature in essence.
In this paper, we present a multi-level connection and adaptive regional attention network (MARA-Net) to properly restore the original background textures in rainy images.
Wavelet transform and the inverse wavelet transform are substituted for down-sampling and up-sampling so feature maps from the wavelet transform and convolutions contain different frequencies and scales.
Image enhancement from degradation of rainy artifacts plays a critical role in outdoor visual computing systems.
Although supervised deep deraining networks have obtained impressive results on synthetic datasets, they still cannot obtain satisfactory results on real images due to weak generalization of rain removal capacity, i. e., the pre-trained models usually cannot handle new shapes and directions that may lead to over-derained/under-derained results.