Image-Specific Prior Adaptation for Denoising

Xin Lu
The Pennsylvania State University

Zhe Lin, Hailin Jin, Jianchao Yang
Adobe Systems Inc.

James Z. Wang
The Pennsylvania State University

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 analyses 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. 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.


Full Paper

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Citation: Xin Lu, Zhe Lin, Hailin Jin, Jianchao Yang and James Z. Wang, ``Image-Specific Prior Adaptation for Denoising,'' IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 5469-5478, 2015.

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Last Modified: August 31, 2015
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