Image_2_Probabilistic Noise2Void: Unsupervised Content-Aware Denoising.PNG (607.47 kB)
Download file

Image_2_Probabilistic Noise2Void: Unsupervised Content-Aware Denoising.PNG

Download (607.47 kB)
posted on 2020-02-19, 04:08 authored by Alexander Krull, Tomáš Vičar, Mangal Prakash, Manan Lalit, Florian Jug

Today, Convolutional Neural Networks (CNNs) are the leading method for image denoising. They are traditionally trained on pairs of images, which are often hard to obtain for practical applications. This motivates self-supervised training methods, such as Noise2Void (N2V) that operate on single noisy images. Self-supervised methods are, unfortunately, not competitive with models trained on image pairs. Here, we present Probabilistic Noise2Void (PN2V), a method to train CNNs to predict per-pixel intensity distributions. Combining these with a suitable description of the noise, we obtain a complete probabilistic model for the noisy observations and true signal in every pixel. We evaluate PN2V on publicly available microscopy datasets, under a broad range of noise regimes, and achieve competitive results with respect to supervised state-of-the-art methods.