@ARTICLE{
iet:/content/journals/10.1049/el.2017.1892,
author = {Rui Chen},
affiliation = { National Engineering Laboratory for Video Technology, Peking University, Beijing 100871, People's Republic of China },
author = {Huizhu Jia},
affiliation = { National Engineering Laboratory for Video Technology, Peking University, Beijing 100871, People's Republic of China },
author = {Xiange Wen},
affiliation = { National Engineering Laboratory for Video Technology, Peking University, Beijing 100871, People's Republic of China },
author = {Xiaodong Xie},
affiliation = { National Engineering Laboratory for Video Technology, Peking University, Beijing 100871, People's Republic of China },
keywords = {RGB channel;variational MC model;Bayer image demosaicking method;image colour analysis;optimised mean-curvature model;G-image surface restoration;interpolation;image resolution;cross-channel aliasing;linear MC model;},
ISSN = {0013-5194},
language = {English},
abstract = {Colour artefacts of demosaicked images are often found at contours due to interpolation across edges and cross-channel aliasing. To tackle this problem, a novel demosaicking method to reliably reconstruct colour channels of a Bayer image based on two different optimised mean-curvature (MC) models is proposed. The missing pixel values in green (G) channel are first estimated by minimising a variational MC model. The curvatures of restored G-image surface are approximated as a linear MC model which guides the initial reconstruction of red (R) and blue (B) channels. Then a refinement process is performed to interpolate accurate full-resolution R and B images. Experiments on benchmark images have testified to the superiority of the proposed method in terms of both the objective and subjective quality.},
title = {Bayer demosaicking using optimised mean curvature over RGB channels},
journal = {Electronics Letters},
issue = {17},
volume = {53},
year = {2017},
month = {August},
pages = {1190-1192(2)},
publisher ={Institution of Engineering and Technology},
copyright = {© The Institution of Engineering and Technology},
url = {https://digital-library.theiet.org/;jsessionid=2g66lfbtcj0tt.x-iet-live-01content/journals/10.1049/el.2017.1892}
}