Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24729
Title: Texture enhanced image denoising via gradient histogram preservation
Authors: Zuo, W
Zhang, L 
Song, C
Zhang, D 
Issue Date: 2013
Source: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2013, p. 1203-1210 How to cite?
Abstract: 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.
Description: 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013, Portland, OR, 23-28 June 2013
URI: http://hdl.handle.net/10397/24729
ISSN: 1063-6919
DOI: 10.1109/CVPR.2013.159
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