ERL-Net: Entangled Representation Learning for Single Image De-Raining

ICCV 2019 Guoqing Wang Changming Sun Arcot Sowmya

Despite the significant progress achieved in image de-raining by training an encoder-decoder network within the image-to-image translation formulation, blurry results with missing details indicate the deficiency of the existing models. By interpreting the de-raining encoder-decoder network as a conditional generator, within which the decoder acts as a generator conditioned on the embedding learned by the encoder, the unsatisfactory output can be attributed to the low-quality embedding learned by the encoder... (read more)

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