A Review Paper: Noise Models in Digital Image Processing

Noise is always presents in digital images during image acquisition, coding, transmission, and processing steps. Noise is very difficult to remove it from the digital images without the prior knowledge of noise model. That is why, review of noise models are essential in the study of image denoising techniques. In this paper, we express a brief overview of various noise models. These noise models can be selected by analysis of their origin. In this way, we present a complete and quantitative analysis of noise models available in digital images.

[1]  Stephen A. Dyer,et al.  Digital signal processing , 2018, 8th International Multitopic Conference, 2004. Proceedings of INMIC 2004..

[2]  Brijendra Kumar Joshi,et al.  Image denoising using wavelet transform and median filtering , 2013, 2013 Nirma University International Conference on Engineering (NUiCONE).

[3]  J. Astola,et al.  Fundamentals of Nonlinear Digital Filtering , 1997 .

[4]  Brijendra Kumar Joshi,et al.  Image denoising using wavelet transform and wiener filter based on log energy distribution over Poisson-Gaussian noise model , 2014, 2014 IEEE International Conference on Computational Intelligence and Computing Research.

[5]  Christopher W. Smith,et al.  Measuring Boltzmann's Constant Using Video Microscopy of Brownian Motion , 2003 .

[6]  Farrokh Marvasti,et al.  Fast restoration of natural images corrupted by high-density impulse noise , 2013, EURASIP Journal on Image and Video Processing.

[7]  Richard Travis Behrens.  Subspace signal processing structured noise , 1991 .

[8]  Aravinthan D. T. Samuel,et al.  Inexpensive microscopy for introductory laboratory courses , 2009 .

[9]  David Zhang,et al.  Two-stage image denoising by principal component analysis with local pixel grouping , 2010, Pattern Recognit..

[10]  Manohar Koli,et al.  Literature Survey on Impulse Noise Reduction , 2013 .

[11]  D. Okada,et al.  Digital Image Processing for Medical Applications , 2009 .

[12]  Versha Rani,et al.  A BRIEF STUDY OF VARIOUS NOISE MODEL AND FILTERING TECHNIQUES , 2013 .

[13]  Thierry Blu,et al.  Image Denoising in Mixed Poisson–Gaussian Noise , 2011, IEEE Transactions on Image Processing.

[14]  Bruce A. Sherwood,et al.  Matter and Interactions , 2001 .

[15]  M. Sandhu,et al.  Performance Comparison of Transform Domain for Speckle Reduction in Ultrasound Image Tajinder kaur , 2012 .

[16]  BROWNIAN MOTION-PAST AND PRESENT , 2005, cond-mat/0511389.

[17]  Jerry D. Gibson,et al.  Handbook of Image and Video Processing , 2000 .

[18]  Nicole Fassbinder.  Techniques For Image Processing And Classification In Remote Sensing , 2016 .

[19]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[20]  Ashley R. Carter,et al.  Improving the quantification of Brownian motion , 2013 .

[21]  Alessandro Foi,et al.  Optimal Inversion of the Generalized Anscombe Transformation for Poisson-Gaussian Noise , 2013, IEEE Transactions on Image Processing.

[22]  Dr. S. R. Chougule,et al.  COMPARATIVE STUDY OF IMAGE DENOISING TECHNIQUES , 2016 .

[23]  Brijendra Kumar Joshi,et al.  Impact of Wavelet Transform and Median Filtering on removal of Salt and Pepper Noise in Digital Images , 2014, 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT).

[24]  Faouzi Benzarti,et al.  Speckle Noise Reduction in Medical Ultrasound Images , 2013, ArXiv.

[25]  S. Mallat.  A wavelet tour of signal processing , 1998 .