Color Image Demosaicking Using a 3-Stage Convolutional Neural Network Structure

Color demosaicking (CDM) is a critical first step for the acquisition of high-quality RGB images with single chip cameras. Conventional CDM approaches are mostly based on interpolation schemes and hand-crafted image priors, which result in unpleasant visual artifacts in some cases. Motivated by the special characteristics of inter-channel correlations (higher correlations for R/G and G/B channels than that for R/B), in this paper, a 3-stage convolutional neural network (CNN) structure for CDM is proposed. In the first stage, the G channel is reconstructed independently. Then, by using the reconstructed G channel as guidance, the R and B channels are recovered in the second stage. Finally, high-quality RGB color images are reconstructed in the third stage. The objective and visual quality evaluation results show that the proposed structure achieves noticeable quality improvements in comparison to the state-of-the-art approaches.