Kernel Regression based image denosing

        In this paper, we make contact with the field of non-parametric statistics and present a development and generalization of tools and results for use in image processing and reconstruc-tion. In particular, we adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, fusion, and more. Furthermore, we establish key relationships with some pop-ular existing methods and show how several of these algorithms, including the recently popularized bilateral filter, are special cases of the proposed framework. The resulting algorithms and analyses are amply illustrated with practical examples