Perceptual Loss with Fully Convolutional for Image Residual Denoising

In this paper we propose a fully convolutional encoder-decoder framework for image residual transformation tasks. Instead of only using per-pixel loss function, the proposed framework learn end-to-end mapping combined with perceptual loss function that depend on low-level features from a pre-trained network. Pointing out the mapping function in order to handle noise-free image by introduce identity mapping. And through an analysis of the interplay between the neural networks and the underlying noisy distribution which they seeking to learn. We also show how to construct a uniform transform, which is then used to make a single deep neural network work well across different levels of noise. Comparing with previous approaches, ours achieves better performance. The experimental results indicate the efficiency of the proposed algorithm to cope with image denoising tasks.

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