Cascaded Convolutional Generator Networks For Solving Imaging Inverse Problems

Abstract

Image restoration or generation covers a range of important image inverse problems that aim to enhance a degraded image to obtain a restored image dataset. Several techniques based on deep learning have been developed for solving inverse problems. However, these methods require large training data sets to obtain a model with a good recovery performance. Recently, deep image prior (DIP) has emerged as a network-based approach that exploits the image representation power of convolutional neural networks (CNN) without resorting to the training stage but requiring the knowledge of the degradation models that describe the degraded observations. In this work, we propose a cascaded convolutional generator network that estimates the target image from degraded observations at an intermediate network stage. Furthermore, the proposed network architecture learns the degradation model by downscaling the recovered image adding relevant information in the restoration process. This approach was implemented to solve three imaging inverse problems: inpainting, deblurring, and super-resolution. The experimental results demonstrate the remarkable performance of the proposed approach, improving the DIP recovery under different scenarios.