Robust Representation Learning with Feedback for Single Image Deraining

Chenghao Chen, Hao Li

A deraining network may be interpreted as a condition generator. Image degradation generated by the deraining network can be attributed to defective embedding features that serve as conditions. Existing image deraining methods usually ignore uncertainty-caused model errors that lower embedding quality and embed low-quality features into the model directly. In contrast, we replace low-quality features by latent high-quality features. The spirit of closed-loop feedback in the automatic control field is borrowed to obtain latent high-quality features. A new method for error detection and feature compensation is proposed to address model errors. Extensive experiments on benchmark datasets as well as specific real datasets demonstrate the advantage of the proposed method over recent state-of-the-art methods.

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