Most common cameras use a CCD sensor device measuring a single color per pixel. The other two color values of each pixel must be interpolated from the neighboring pixels in the so-called demosaicking process.
State-of-the-art demosaicking algorithms take advantage of inter-channel correlation locally selecting the best interpolation direction. These methods give impressive results except when local geometry cannot be inferred from neighboring pixels or channel correlation is low. In these cases, they create interpolation artifacts.
We introduced in  an algorithm involving non-local image self-similarity in order to reduce interpolation artifacts when local geometry is ambiguous (algorithm can be tested at ipol demo).
We recently introduced in  a new algorithm taking advantage of spectral correlation at the time as non local self similarity. The proposed algorithm also introduces a clear and intuitive manner of balancing how much channel-correlation must be taken advantage of. In such a way, the algorithm performs correctly independently of the image database used for testing.
 A. Buades, B. Coll, J.M Morel, C. Sbert “Self similarity driven demosaicking”, IEEE Transactions on Image Processing, Vol. 18(6), pp:1192-1202, 2009.
 J. Duran. A. Buades, “Self-Similarity and Spectral Correlation Adaptive Algorithm for Color Demosaicking”, IEEE Transactions on Image Processing, Vol. 23(9), pp. 4031 – 4040, 2014.