Example based demosaicing

Abstract

Demosaicing is an algorithm used to reconstruct a color image from the incomplete color samples of a color filter array (CFA). Most demosaicing algorithms can be broadly classified into spatial-domain and frequency-domain approaches. Despite significant progress in the past decade, current state of the art demosaicing algorithms still tend to produce artifacts at high-saturation edges. In this paper we propose a new approach to demosaicing -example based. Comparative experimental evaluation shows that example-based demosaicing (EBD) produces visually superior, artifact-free results.

Demosaicing of Color Images by Accurate Estimation of Luminance

“…A detailed report on the types of artifacts observed in demosaiced images is presented in [13]. To minimize occurrences of artifacts in demosaiced images, the use knowledge derived from of local region or local patches [14][15][16][17] to interpolate/estimate the missing color components is proposed. An iterative demosaicing technique is essential to derive local knowledge and achieve accurate estimation [18,19].…”

Deep Learning Approach for Image Denoising and Image Demosaicing

“…Demand of high de-noising quality, many methodologies of de-noising suffers from the two major drawbacks. First one is, most of the de-noising methodologies involves problem in complex optimization in testing stage, which causes more time in de-noising process [17], [18]. Hence, most of methodologies can rarely achieve the high performance short of sacrificing the computational efficiency.…”