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キーワード:
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要旨:
Image denoising can be described as the problem of
mapping from a noisy image to a noise-free image. The
best currently available denoising methods approximate
this mapping with cleverly engineered algorithms. In this
work we attempt to learn this mapping directly with a plain
multi layer perceptron (MLP) applied to image patches.
While this has been done before, we will show that by training on large image databases we are able to compete with the current state-of-the-art image denoising methods. Furthermore, our approach is easily adapted to less extensively studied types of noise (by merely exchanging the training data), for which we achieve excellent results as well.
