This module allows you to train and use deep learning based models for the denoising of images. This method can be applied to any type of image and any dimensions and it is not dedicated to a special field of application. It should be used before applying processing functions that modify the pixel values to that image.

Denoising is an operation to reduce noise in an image, in case of Intellesis Denoising with the help of deep learning methods. In general, there are different ways to train a denoising model. Intellesis Denoising uses the approach called Noise2Void (N2V), which requires only a noisy input image for the training of a model and can thus be trained directly on the data that should be denoised. To give a simplistic explanation, this N2V method replaces pixels by masked pixel data randomly selected within a certain window/surrounding area. With this approach, the model is then trained to reconstruct the original pixels and to discard the implicit noise in the image. For detailed information on Noise2Void , see the paper "Noise2Void - Learning Denoising from Single Noisy Images" written by Alexander Krull, Tim-Oliver Buchhol and Florian Jug, see also https://arxiv.org/abs/1811.10980.