Texture enhanced image denoising via gradient histogram preservation

Wangmeng Zuo, Lei Zhang, Chunwei Song, Dapeng Zhang

Research output: Journal article publicationConference articleAcademic researchpeer-review

82 Citations (Scopus)


Image denoising is a classical yet fundamental problem in low level vision, as well as an ideal test bed to evaluate various statistical image modeling methods. One of the most challenging problems in image denoising is how to preserve the fine scale texture structures while removing noise. Various natural image priors, such as gradient based prior, nonlocal self-similarity prior, and sparsity prior, have been extensively exploited for noise removal. The denoising algorithms based on these priors, however, tend to smooth the detailed image textures, degrading the image visual quality. To address this problem, in this paper we propose a texture enhanced image denoising (TEID) method by enforcing the gradient distribution of the denoised image to be close to the estimated gradient distribution of the original image. A novel gradient histogram preservation (GHP) algorithm is developed to enhance the texture structures while removing noise. Our experimental results demonstrate that the proposed GHP based TEID can well preserve the texture features of the denoised images, making them look more natural.
Original languageEnglish
Article number6619003
Pages (from-to)1203-1210
Number of pages8
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Publication statusPublished - 15 Nov 2013
Event26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 - Portland, OR, United States
Duration: 23 Jun 201328 Jun 2013

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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