Self-similarity driven color demosaicking

Abstract : Demosaicking is the process by which from a matrix of colored pixels measuring only one color component per pixel, red, green or blue, one can infer a whole color information at each pixel. This inference requires a deep understanding of the interaction between colors, and the involvement of image local geometry. Although quite successful in making such inferences with very small relative error, state of the art demosaicking methods fail when the local geometry cannot be inferred from the neighboring pixels. In such a case, which occurs when thin structures or fine periodic patterns were present in the original, state of the art methods can create disturbing artifacts, known as zipper effect, blur, and color spots. The aim of this paper is to show that these artifacts can be avoided by involving the image self-similarity to infer missing colors. Detailed experiments show that a satisfactory solution can be found, even for the most critical cases. Extensive comparisons with state of the art algorithms will be performed on two different classic image databases.
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IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2009, 18 (7), pp.1192-1202
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Antoni Buades, Bartomeu Coll, J.M Morel, Catalina Sbert. Self-similarity driven color demosaicking. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2009, 18 (7), pp.1192-1202. 〈hal-00449362〉

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