DTDeMo: A Deep Learning-Based Two-Stage Image Demosaicing Model With Interpolation and Enhancement
Jingchao Hou, Garas Gendy, Guo Chen, Liangchao Wang, Guanghui He
Abstract
Image demosaicing is one of the most ubiquitous and performance-critical image processing tasks. However, traditional demosaicing methods use fixed weights to finish the interpolation, while deep learning demosaicing restoration always breaks the image array arrangement rule, and they can't fully use the existing pixel information. To rectify these weaknesses, in this paper, we propose the convolution interpolation block (CIB) to obey the RAW data arrangement rule and the deep demosaicing residual block (DDRB) to repeatedly utilize existing pixel information for demosaicing. Based on the CIB and DDRB, we present a novel two-stage demosaicing model (DTDeMo), including differential interpolation and enhancement processes. Specifically, the interpolation process contains several CIBs and DDRBs with trainable interpolation parameters. Meanwhile, the enhancement process consists of a transformer-based block and a series of DDRBs to enhance the interpolation results. The effectiveness of CIBs, DDRBs, the proposed interpolation process, and the enhancement process is confirmed through an ablation study. A thorough comparison with several methods shows that our DTDeMo outperforms state of the art quantitatively and qualitatively.