文献类型：期刊文献

英文题名：Joint Image Denoising and Demosaicking by Low Rank Approximation and Color Difference Model

作者：Zhai, Xia[1];Guo, Weiwei[2];Zhang, Yongqin[3];Xiao, Jinsheng[4];Hu, Xiaoguang[5]

通讯作者：Zhai, X[1]

机构：[1]Henan Univ Econ & Law, Arts Dept, Zhengzhou, Henan, Peoples R China;[2]Univ Elect Sci & Technol, Dept Informat & Commun Engn, Chengdu, Sichuan, Peoples R China;[3]Northwest Univ, Sch Informat Sci & Technol, Xian, Shaanxi, Peoples R China;[4]Wuhan Univ, Sch Elect Informat, Wuhan, Peoples R China;[5]Peoples Publ Secur Univ China, Sch Criminal Sci & Technol, Beijing, Peoples R China

第一机构：河南财经政法大学艺术学院

通讯机构：[1]corresponding author), Henan Univ Econ & Law, Arts Dept, Zhengzhou, Henan, Peoples R China.|[1048417]河南财经政法大学艺术学院;[10484]河南财经政法大学;

年份：2017

卷号：14

期号：1

起止页码：1-7

外文期刊名：INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY

收录：;Scopus(收录号：2-s2.0-85007579920);WOS:【SCI-EXPANDED(收录号:WOS:000392560800001)】；

语种：英文

外文关键词：Image denoising; image demosaicking; CFA; low rank approximation; color difference model

摘要：Digital cameras generally use a single image sensor which surface is covered by a color filter array. The Color Filter Array (CFA) limits each sensor pixel by sampling one of the three primary color values (red, green or blue), whereas the other two missing color values would be acquired by the post-processing procedure called demosaicking. From the noisy CFA data, the full color images are reconstructed through an imaging pipeline of demosaicking and denoising. However, image denoising in the RGB space has expensive computation cost. In this paper, to increase the efficiency and the color fidelity, we propose a novel joint denoising and demosaicking strategy to reconstruct the noiseless full color image from the input noisy CFA data. The low-rank approximation technique is first used to remove the noise from CFA data. Then, image demosaicking using both color difference space and signal correlation are applied to the denoised CFA data to obtain the noise-less full color image. The experimental results show that the proposed algorithm not only improves the quality of full color image but also outperforms the existing state-of-the-art methods both subjectively and objectively.