Demosaicking Algorithm Using Deep Residual Convolutional Networks
Jin Wang, Hyunbum Kim, Gwanggil Jeon · 2024
Lots of mobile devices such as digital camera, mobile phones, and personal digital assistants (PDAs) adopt single image sensors to acquire scene images for real-time processing. Convolutional neural networks (CNN) have been shown good performance in lots of area about image processing. In this paper, we develop a demosaicking method based on deep residual CNN (DRCNN). Firstly, we initialize the mosaicked images using conventional demosaicking algorithms and learn the DRCNN for three color channels. In DRCNN, we adopt binary convolution unit (BCU), efficient layer aggregation networks (ELAN), and dense residual blocks (DRB). Experimental results show that our proposed algorithms outperforms existing approaches in PSNR, computational complexity, and visual quality.