Open AccessBayer demosaicking using optimised mean curvature over RGB channels
Author(s) -
Chen Rui,
Jia Huizhu,
Wen Xiange,
Xie Xiaodong
Publication year - 2017
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2017.1892
Subject(s) - demosaicing , interpolation (computer graphics) , artificial intelligence , curvature , rgb color model , benchmark (surveying) , channel (broadcasting) , mathematics , computer vision , aliasing , pixel , iterative reconstruction , image (mathematics) , computer science , filter (signal processing) , image processing , color image , geometry , geology , computer network , geodesy
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.