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


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.

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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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