Image Denoising and the Generative Accumulation of Photons | Alexander Krull скачать в хорошем качестве

Image Denoising and the Generative Accumulation of Photons | Alexander Krull

Abstract Shot noise is a fundamental property of many imaging applications, especially in fluorescence microscopy. Removing this noise is an ill-defined problem since many clean solutions exist for a single noisy image. Traditional approaches aiming to map a noisy image to its clean counterpart usually find the minimum mean square error (MMSE) solution,i.e. , they predict the expected value of the posterior distribution of possible clean images. We present a fresh perspective on shot noise corrupted images and noise removal. By viewing image formation as the sequential accumulation of photons on a detector grid, we show that a network trained to predict the where the next photon could arrive is in fact solving the traditional MMSE denoising task. This new perspective allows us to make three contributions: (i) We present a new strategy for self-supervised denoising. (ii) We present a new method for sampling from the posterior of possible solutions by iteratively sampling and adding small numbers of photons to the image. (iii) We derive a full generative model by starting this process from an empty canvas." https://www.dkfz.de/en/datascience/se...