Image-Specific Prior Adaptation for Denoising

Xin Lu, Zhe Lin, Hailin Jin, Jianchao Yang, James Wang

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

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.

Original languageEnglish (US)
Article number7222427
Pages (from-to)5469-5478
Number of pages10
JournalIEEE Transactions on Image Processing
Volume24
Issue number12
DOIs
StatePublished - Dec 1 2015

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design

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