Tuesday, May 06, 2014

GHP : Gradient Histogram Estimation and Preservation for Texture Enhanced Image Denoising - implementation -

Gradient Histogram Estimation and Preservation for Texture Enhanced Image Denoising by Wangmeng 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 for the denoising of images consisting of regions with different textures. An algorithm 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 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.

No comments: