  We propose a deep fully convolutional neural network with a new type of
layer, named median layer, to restore images contaminated by the
salt-and-pepper (s&p) noise. A median layer simply performs median filtering on
all feature channels. By adding this kind of layer into some widely used fully
convolutional deep neural networks, we develop an end-to-end network that
removes the extremely high-level s&p noise without performing any non-trivial
preprocessing tasks, which is different from all the existing literature in s&p
noise removal. Experiments show that inserting median layers into a simple
fully-convolutional network with the L2 loss significantly boosts the
signal-to-noise ratio. Quantitative comparisons testify that our network
outperforms the state-of-the-art methods with a limited amount of training
data. The source code has been released for public evaluation and use
(https://github.com/llmpass/medianDenoise).
