Color Demosaicing Using Structural Instability
Stefan Petersson, Håkan Grahn, Jim Rasmusson · 2016
This paper introduces a new metric for approximating structural instability in Bayer image data. We show that the metric can be used to identify and classify validity of color correlation in local image regions. The metric is used to improve interpolation performance of an existing state-of-the-art single pass linear demosaicing algorithm, with virtually no impact on computational GPGPU complexity and performance. Using four different image sets, the modification is shown to outperform the original method in terms of visual quality, by having an average increase in PSNR of 0.7dB in the red, 1.5dB in the green and 0.6dB in the blue channel respectively. Because of fewer high-frequency artifacts, the average output data size also decreases by 2.5%.