A low-complexity learning-based algorithm for joint application of demosaicking and up-scaling
Kuan-Yu Huang, Pei‐Yin Chen, Ching-Ching Huang · 2020
This paper proposes a low-complexity algorithm based on machine learning to deal with demosaicking and upscaling processes simultaneously. First, the color difference planes are calculated to extract the edge orientation and reconstruct the missing channel of G. Then the correlation of neighboring pixels is adopted to reconstruct the missing channels of RB and refine reconstructed the color planes. Finally, a novel machine learning structure is employ to up-scale image by optical interpolation kernels on the basis of designate feature descriptor. The refined operation and machine learning structure are two crucial contribution in this paper, which bring about decrease of the combination error and improvement of reconstructed quality. In experimental result, it is confirmed that the proposed method gets better reconstructed quality with lower cost of calculation than previous related methods.