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A Survey on Various Image De-noising Methods

More Nayna K., Seema B. Vora


Image de-noising is a classical yet fundamental problem in low level vision, as well as an ideal test bed to evaluate various statistical image modeling methods. The restoration of a blurry or noisy image is commonly performed with a maximum a posteriori probability (MAP) estimator, which maximizes a posterior probability to reconstruct a clean image from a degraded image. A MAP estimator, when used with a sparse gradient image prior, reconstructs piecewise smooth images and typically removes textures that are important for visual realism. One of the most challenging problems in image de-noising 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 de-noising algorithms based on these priors, however, tend to smooth the detailed image textures, degrading the image visual quality. To address this problem, we propose a texture enhanced image de-noising (TEID) method by enforcing the gradient distribution of the de-noised image to be close to the estimated gradient distribution of the original image. Another method is an alternative de-convolution method called iterative distribution reweighting (IDR) which imposes a global constraint on gradients so that are constructed image should have a gradient distribution similar to a reference distribution.

Keywords: types of noise, image de-noising, image deblurring, comparison, proposed algorithm

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“Texture Enhanced Image Denoising via Gradient Histogram Preservation” Wangmeng Zuo, Lei Zhang Chunwei Song David Zhang Harbin Institute of Technology, The Hong Kong Polytechnic University CVPRE2013 is open access version and available in IEEE Xplore.

“Image Restoration by Matching Gradient Distributions” IEEE Transactions On Pattern Analysis And Machine Intelligence 2012; 34(4).

“Hyperspectral Image De-noising Employing a Spectral–Spatial Adaptive IEEE Transactions on Geoscience And Remote Sensing 2012; 50(10).

A Regularized Nonlinear Diffusion Approach for Texture Image Denoising XXII Brazilian Symposium on Computer Graphics and Image Processing 2009 IEEE Wallace Correa de O. Casaca , Maur´ılio Boaventura Department of Computer Science and Statistics Institute

of Biosciences, Humanities and Exact Sciences S˜ao Paulo State University, S˜ao Jos´e do Rio Preto, SP, Brazil.

Proximal Method For Geometry And Texture Image Decomposition Proceedings of 2010 IEEE 17th International Conference on Image Processing L. M. Brice˜no-Arias , P. L. Combettes , J.-C. Pesquet and N. Pustelnik.

“Nonlinear Regularized Reaction-Diffusion Filters for Denoising of Images With Textures” IEEE Transactions On Image Processing 2008 17(8).

Craig S. Lent, Learning to Program with MATLAB Building GUI Tools Germany: Wiley.


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