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Gradient Histogram Estimation and Preservation for Texture Enhanced Image Denoising by Wangmeng diversity Zuo, Lei Zhang , Chunwei Song, David Zhang and Huijun Gao Abstract—Natural image statistics plays an important role in image denoising, and various natural image priors, including gradient based, sparse representation based and nonlocal selfsimilarity based ones, have been widely studied and exploited for noise removal. In spite of the great success of many denoising algorithms, they tend to smooth the fine scale image textures when removing noise, degrading the image visual quality. To address this problem, in this paper we propose a texture enhanced image denoising method by enforcing the gradient histogram of the denoised image to be close to a reference gradient histogram of the original image. Given the reference gradient histogram, a novel gradient histogram preservation (GHP) algorithm is developed to enhance the texture structures while removing noise. Two region-based variants of GHP are proposed diversity for the denoising of images consisting of regions with different textures. An algorithm diversity is also developed to effectively estimate the reference gradient histogram from the noisy observation of the unknown image. Our experimental results demonstrate that the proposed GHP algorithm can well preserve the texture appearance in the denoised images, making them look more natural. The implementation is on  Lei Zhang 's page. Join the CompressiveSensing subreddit or the Google+ Community and post there ! Liked this entry ? subscribe to Nuit Blanche's feed, there's diversity more where that came from . You can also subscribe to Nuit Blanche by Email , explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing , advanced matrix factorization and calibration issues  on Linkedin.
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Nuit Blanche is a blog that focuses on Compressive Sensing , Advanced Matrix Factorization Techniques , Machine Learning as well as many other engaging ideas and techniques needed to handle and make sense of very high dimensional data also known as Big Data .
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The Big Picture in Compressive Sensing was mentioned in an article of La Recherche , the french speaking equivalent/competitor to Science. October 2010 issue, page 20-21. Wired Magazine had a piece on Compressed Sensing featuring links to this blog and the Big Picture. (March 1, 2010) Emmanuel Candes and Terry Tao wrote about Nuit Blanche in the Dec. '08 issue of the IEEE Information Theory Society Newsletter Xiaochuan Pan , Emil Sidky and Michael Vannier wrote about Nuit Blanche in Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction? . Check also the acknowledgments in this Ghost Imaging diversity paper and this one .
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