Learning to restore multiple image degradations simultaneously

Image corruptions are common in the real world, for example images in the wild may come with unknown blur, bias field, noise, or other kinds of non-linear distributional shifts, thus hampering encoding methods and rendering downstream task unreliable. Image upgradation requires a complicated balance between high-level contextualised information and spatial specific details. Existing approaches to solving the problems are designed to focus on single corruption, which unavoidably results in poor performance when the acquisitions suffer from multiple degradations. In this study, we investigate the possibility of handling multiple degradations and enhancing the quality of images via deblurring, bias field correction, and denoising. To tackle the problems with propagating errors caused by independent learning, we propose a unified and scalable framework, which consists of three special decoders. Two decoders learn artifact attention from provided images thereby generating realistic individual artifact and multiple artifacts on single image; the third decoder is trained towards removing artifact on the synthetic image with multiple corruptions thereby generating high quality image. We additionally provide improvements over previous image degradation synthesis approaches by modelling multiple image degradations directly from data observations. We first create a toy MNIST dataset and investigate the properties of the proposed algorithm. We then use brain MRI datasets to demonstrate our method's robustness, including both simulated (where necessary) and real-world artifacts. In addition, our method can be used for single/or multiple degradation(s) synthesis by implementing the learned degradation operators in a new domain from a given dataset. The code will be released upon acceptance of the paper.