Image-Specific Prior Adaptation for Denoising

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Xin LuJames Wang

Abstract

Image priors are essential to many image restoration applications, including denoising, deblurring, and inpainting. Existing methods use either priors from the given image (internal) or priors from a separate collection of images (external). We find through statistical analysis that unifying the internal and external patch priors may yield a better patch prior. We propose a novel prior learning algorithm that combines the strength of both internal and external priors. In particular, we first learn a generic Gaussian mixture model from a collection of training images and then adapt the model to the given image by simultaneously adding additional components and refining the component parameters. We apply this image-specific prior to image denoising. The experimental results show that our approach yields better or competitive denoising results in terms of both the peak signal-to-noise ratio and structural similarity.

References

Sep 21, 2004·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Zhou WangEero P Simoncelli
Dec 13, 2006·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Michael Elad, Michal Aharon
Aug 11, 2007·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Kostadin DabovKaren Egiazarian

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Citations

Jul 15, 2016·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Enming LuoTruong Q Nguyen

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