Bayer Demosaicking Using Optimized Mean Curvature over RGB channels
This addresses color artifacts in digital imaging for applications like photography, but it is incremental as it builds on existing demosaicking techniques.
The paper tackled color artifacts in Bayer demosaicking by proposing a method using optimized mean-curvature models to reconstruct color channels, achieving superior objective and subjective quality on benchmark images.
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.