Efficient regression priors for post-processing demosaiced images

Color demosaicing is a process of reconstructing lost pixels in an incomplete color image. By extracting spatial-spectral correlations of RGB channels various interpolation methods have been proposed with low computational complexity. Meanwhile, optimization strategies such as sparsity and adaptive PCA based algorithm (SAPCA) were developed. SAPCA outperforms many interpolation techniques by impressive margins at the cost of dramatically increasing the computational time. In this paper we propose an efficient novel post-processing algorithm based on the adjusted anchored neighborhood regression (A+) method from image super-resolution literature. We greatly improve the results of the demosaicing methods, and achieve image quality as competitive as SAPCA but orders of magnitude faster.