  Image denoising can be described as the problem of mapping from a noisy image
to a noise-free image. In another paper, we show that multi-layer perceptrons
can achieve outstanding image denoising performance for various types of noise
(additive white Gaussian noise, mixed Poisson-Gaussian noise, JPEG artifacts,
salt-and-pepper noise and noise resembling stripes). In this work we discuss in
detail which trade-offs have to be considered during the training procedure. We
will show how to achieve good results and which pitfalls to avoid. By analysing
the activation patterns of the hidden units we are able to make observations
regarding the functioning principle of multi-layer perceptrons trained for
image denoising.
