Nonlocal-Similarity Algorithm for Color Image Demosaicing

Image demosaicing is the process by which from a single CCD sensor recording only one color sample at each pixel, a full color information per pixel can be inferred. Most image demosaicing methods assume the high local spectral correlation in estimating the missing color components. However, such an assumption may fail for images with high color saturation and sharp color transitions. Meanwhile, self-similarity, which means that the pixels at different locations resemble with each other, is a fundamental property of an image. In this paper, the non-local similarity information provided by an image itself is made use of demosaicing on the McMaster dataset with lower local redundancy. First, the most similar nonlocal pixels to the estimated pixel are searched. Then, according to the similar degree and the smooth degree, the image patch is adaptively chosen to estimate the missing color samples. Experimental results show that the presented algorithm is able to improve the PSNR, sharpen texture and edge of the image and lead to higher visual quality of reproduced color images.