Bayer Demosaicking Using Optimized Mean Curvature over RGB channels

TL;DR

This paper introduces a novel demosaicking method that uses optimized mean curvature models to improve color reconstruction in Bayer images, reducing artifacts at edges and across channels.

Contribution

It proposes a new variational mean-curvature based approach for more accurate color channel reconstruction in Bayer demosaicking.

Findings

Outperforms existing methods in objective quality metrics.

Produces visually superior images with fewer artifacts.

Effective in reconstructing high-quality full-resolution color images.

Abstract

Color artifacts of demosaicked images are often found at contours due to interpolation across edges and cross-channel aliasing. To tackle this problem, we propose a novel demosaicking method to reliably reconstruct color channels of a Bayer image based on two different optimized mean-curvature (MC) models. The missing pixel values in green (G) channel are first estimated by minimizing 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.