title: Image-Specific Prior Adaptation for Denoising.
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.
authors: Xin
authors: X
authors: Lu
authors: Zhe
authors: Z
authors: Lin
authors: Hailin
authors: H
authors: Jin
authors: Jianchao
authors: J
authors: Yang
authors: James Z
authors: JZ
authors: Wang