FULLY UNSUPERVISED DIVERSITY DENOISING WITH CONVOLUTIONAL VARIATIONAL AUTOENCODERS

Abstract

Deep Learning based methods have emerged as the indisputable leaders for virtually all image restoration tasks. Especially in the domain of microscopy images, various content-aware image restoration (CARE) approaches are now used to improve the interpretability of acquired data. Naturally, there are limitations to what can be restored in corrupted images, and like for all inverse problems, many potential solutions exist, and one of them must be chosen. Here, we propose DIVNOISING, a denoising approach based on fully convolutional variational autoencoders (VAEs), overcoming the problem of having to choose a single solution by predicting a whole distribution of denoised images. First we introduce a principled way of formulating the unsupervised denoising problem within the VAE framework by explicitly incorporating imaging noise models into the decoder. Our approach is fully unsupervised, only requiring noisy images and a suitable description of the imaging noise distribution. We show that such a noise model can either be measured, bootstrapped from noisy data, or co-learned during training. If desired, consensus predictions can be inferred from a set of DIVNOISING predictions, leading to competitive results with other unsupervised methods and, on occasion, even with the supervised state-of-the-art. DIVNOISING samples from the posterior enable a plethora of useful applications. We are piq showing denoising results for 13 datasets, piiq discussing how optical character recognition (OCR) applications can benefit from diverse predictions, and are piiiq demonstrating how instance cell segmentation improves when using diverse DIVNOISING predictions.

1. INTRODUCTION

The goal of scientific image analysis is to analyze pixel-data and measure the properties of objects of interest in images. Pixel intensities are subject to undesired noise and other distortions, motivating an initial preprocessing step called image restoration. Image restoration is the task of removing unwanted noise and distortions, giving us clean images that are closer to the true but unknown signal. In the past years, Deep Learning (DL) has enabled tremendous progress in image restoration (Mao et al., 2016; Zhang et al., 2017b; Zhang et al., 2017; Weigert et al., 2018) . Supervised DL methods use corresponding pairs of clean and distorted images to learn a mapping between the two quality levels. The utility of this approach is especially pronounced for microscopy image data of biological samples (Weigert et al., 2017; 2018; Ouyang et al., 2018; Wang et al., 2019) , where quantitative downstream analysis is essential. More recently, unsupervised content-aware image restoration ˚Shared first authors. : Shared last authors.

