Image-Specific Prior Adaptation for Denoising

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

Research output: Contribution to journalArticle

7 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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