Image Denoising by using Modified SGHP Algorithm

In real time applications, image denoising is a predominant task. This task makes adequate preparation for images looks prominent. But there are several denoising algorithms and every algorithm has its own distinctive attribute based upon different natural images. In this paper, we proposed a perspective that is modified parameter in S-Gradient Histogram Preservation denoising method. S-Gradient Histogram Preservation is a method to compute the structure gradient histogram from the noisy observation by taking different noise standard deviations of different images. The performance of this method is enumerated in terms of peak signal to noise ratio and structural similarity index of a particular image. In this paper, mainly focus on peak signal to noise ratio, structural similarity index, noise estimation and a measure of structure gradient histogram of a given image.

[1]  Lei Zheng,et al.  Image Noise Level Estimation by Principal Component Analysis , 2013, IEEE Transactions on Image Processing.

[2]  Ian Burns,et al.  Measuring texture classification algorithms , 1997, Pattern Recognit. Lett..

[3]  D. Makovoz.  Noise Variance Estimation In Signal Processing , 2006, 2006 IEEE International Symposium on Signal Processing and Information Technology.

[4]  Md. Masud Rana,et al.  Performance Study of Soft Local Binary Pattern over Local Binary Pattern under Noisy Images , 2016 .

[5]  P. P. Koltsov.  Comparative study of texture detection and classification algorithms , 2011 .

[6]  Olusola Abayomi-Alli,et al.  Facial Image Verification and Quality Assessment System -FaceIVQA , 2013 .

[7]  Sebastian Nowozin,et al.  Loss-Specific Training of Non-Parametric Image Restoration Models: A New State of the Art , 2012, ECCV.

[8]  Stefan Harmeling,et al.  Image denoising: Can plain neural networks compete with BM3D? , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[10]  M. Vehvilainen,et al.  New method for noise estimation in images , 2005 .

[11]  Soosan Beheshti,et al.  Adaptive Noise Variance Estimation in BayesShrink , 2010, IEEE Signal Processing Letters.

[12]  Tolga Tasdizen.  Principal components for non-local means image denoising , 2008, 2008 15th IEEE International Conference on Image Processing.

[13]  Aihong Liu,et al.  A Fast Method of Estimating Gaussian Noise , 2009, 2009 First International Conference on Information Science and Engineering.

[14]  Sreedhar Kollem,et al.  A New Approach to Image Denoising by Patch-Based Algorithm , 2016 .

[15]  Pankaj Hedaoo,et al.  Wavelet Thresholding Approach For Image Denoising , 2011 .

[16]  Lei Zhang,et al.  Nonlocally Centralized Sparse Representation for Image Restoration , 2013, IEEE Transactions on Image Processing.

[17]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[18]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[19]  Vladimir V. Lukin,et al.  Image Informative Maps for Estimating Noise Standard Deviation and Texture Parameters , 2011, EURASIP J. Adv. Signal Process..

[20]  Muhammad Sharif,et al.  A Review of Image Denoising Methods , 2015 .

[21]  Charles Kervrann,et al.  Optimal Spatial Adaptation for Patch-Based Image Denoising , 2006, IEEE Transactions on Image Processing.

[22]  Y MuraliMohanBabu.,et al.  A New Approach for SAR Image Denoising , 2015 .

[23]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[24]  Xinhao Liu,et al.  Noise level estimation using weak textured patches of a single noisy image , 2012, 2012 19th IEEE International Conference on Image Processing.

[25]  Dominique Pastor,et al.  A theoretical result for processing signals that have unknown distributions and priors in white Gaussian noise , 2008, Comput. Stat. Data Anal..