Texture Enhanced Image Denoising via Gradient Histogram Preservation

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Authors Wangmeng Zuo, Lei Zhang, Chunwei Song, and David Zhang
Journal/Conference Name Conference on Computer Vision and Pattern Recognition (CVPR 2013)
Paper Category
Paper 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 pa-per we propose a texture enhanced image denoising (TEID)method by enforcing the gradient distribution of the de-noised 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.
Date of publication 2013
Code Programming Language MATLAB
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