Joint Demosaicking and Blind Deblurring Using Deep Convolutional Neural Network

Abstract

Despite extensive research efforts, blind image deblurring remains a challenge without general robust solutions. A long-overlooked problem of existing deblurring methods is that they are all designed to work on fully sampled RGB input images for simplicity. But, in practice, most RGB color images are reconstructed from Bayer mosaic data hence riddled with various high-frequency demosaicking artifacts, such as zippering and moiré patterns, which can easily derail a deblurring algorithm. In this paper, we propose a novel multi-scale deep convolutional neural network to solve demosaicking and deblurring jointly. By processing Bayer raw images directly, our method is free of the interference of demosaicking artifacts. Extensive experiments show that the joint approach greatly outperforms the simple cascade of state-of-art demosaicking and deblurring methods.