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Deep Residual Networks for Impulsive Noise Suppression

We introduce a new method of noise suppression using fully convolutional neural networks for salt and pepper noise. We adopt a well-known residual learning framework to get convergence faster in the training phase than conventional learning. Based on experimental results, our proposed method can outperform the state-of-the-arts method at most levels of noise contamination. In addition, our method is qualitatively proven to maintain the details of the denoised image even at extreme contamination level.