# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1705.06300