Channel-Wise Predictive Filter Flow for Demosaicking

Abstract

Demosaicking is an image reconstruction process to recover a full color image from color filter array (CFA) mosaic data. Recently, deep convolutional neural network (CNN)-based demosaicking methods have been explored and have achieved state-of-the-art accuracy. In the deep-CNN-based demosaicking, output pixels are affected by a large spatial region; however, the information involved in demosaicking often only exists locally. In this paper, we propose a channel-wise predictive filter flow (PFF) for demosaicking. Since the PFF is a model that predicts a space-variant linear filter that is transformed to the target image by linearly combining it with the input image, target pixels are reconstructed only from local information. To incorporate the PFF into demosaicking, the proposed network synthesizes the filter flow corresponding to each channel by different networks that are learned independently. Experimental results demonstrate that the proposed method provides better or competitive results compared with several state-of-the-art deep-CNN-based demosaicking algorithms.