Comparative Study of Image Denoise Algorithms

Tram Tran Nguyen Quynh, Hung Do Phi

Abstract


Denoising is a pre-processing step in digital image processing system. It is also typical image processing challenges. Many works proposed to solve problem with new approaching. They can be divided into two main categories: spatial-based or transform-based. Some denoising methods apply in both spatial and transform domains. The goal of this paper focuses on reviewing denoise methods, classifying them into different categories, and identifying new trends. Moreover, we do experiments to compare pros, cons of methods in survey.


Keywords


Denoise; Bilateral Filter; Guided Image Filter; ROF; TV-L1; Total Variation; Wavelet shrinkage; Dual-domain image denoise; Non-local dual denoising.

Full Text:

PDF

References


M. Motwani, M. Gadiya, R. Motwani, and F. Harris Jr, “Survey of image denoising techniques,” in Proceedings of GSPx, 2004, pp. 27 30.

C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), pp. 839–846, 1998. [Online]. Available: http://ieeexplore.ieee.org/document/710815/

S. Paris, P. Kornprobst, J. Tumblin, and F. Durand, “Bilateral Filtering: Theory and Applications,” Foundations and Trends® in Computer Graphics and Vision, vol. 4, no. 1, pp. 1–75, 2008. [Online]. Available: http://www.nowpublishers.com/article/Details/CGV-020

R. C. Gonzalez and R. E. Woods, Digital Image Processing (3rd Edition). Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 2006.

K. He, J. Sun, and X. Tang, “Guided image filtering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 6, pp. 1397–1409, 2013.

L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D, vol. 60, no. 1-4, pp. 259–268, 1992.

A. Chambolle, V. Caselles, D. Cremers, M. Novaga, and T. Pock, “An Introduction to Total Variation for Image Analysis,” Theoretical foundations and numerical methods for sparse recovery, vol. 9, pp. 263–340, 2010.

I. K. Fodor and C. Kamath, “Denoising through wavelet shrinkage: an empirical study,” Journal of Electronic Imaging, vol. 12, no. 1, pp. 151–160, 2003.

C. Knaus and M. Zwicker, “Dual-domain image denoising,” IEEE International Conference on Image Processing, ICIP 2013 Proceedings, no. 4, pp. 440–444, 2013.

K. Dabov, R. Foi, V. Katkovnik, and K. Egiazarian, “BM3D image denoising with shape-adaptive principal component analysis,” Proc. Workshop on Signal Processing with Adaptive Sparse Structured Representations, p. 6, 2009.

M. Lebrun, “An Analysis and Implementation of the BM3D Image Denoising Method,” Image Processing On Line, vol. 2, no. May, pp.175–213, 2012. [Online]. Available: http://www.ipol.im/pub/art/2012/l- bm3d/?utm_source=doi

N. Pierazzo, M. Lebrun, M. E. Rais, J. M. Morel, and G. Facciolo, “Non-local dual image denoising,” in 2014 IEEE Internationa Conference on Image Processing, ICIP 2014, 2014, pp. 813–817.

A.Mordvintsev.(2013)Rofandtv-l1denois-ingwithprimal-dualalgorithm.[Online].Available: https://github.com/znah/notebooks/blob/master/TV_denoise.ipynb

J. Duran, B. Coll, and C. Sbert, “Chambolle’s Projection Algorithm for Total Variation Denoising,” Image Processing On Line, vol. 2013, pp.311–331, 2013.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image restoration by sparse 3D transform-domain collaborative filtering,” Proceedings of SPIE-IS&T, Image Processing: Algorithms and Systems VI, San Jose, California, USA, 28 January 2008, vol. 6812, no. 213462, p. 12, 2008.

K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, and P. O. Box, “A Non-local and Shape-Adaptive Transform-Domain Collaborative Filtering,” Proc. Int. Workshop on Local and Non-Local Approx. in Image Process., no. x, p. 9, 2008.

J. Allen, “Short term spectral analysis, synthesis, and modification by discrete Fourier transform,” Acoustics, Speech and Signal Processing, IEEE Transactions on, vol. 25, no. 3, pp. 235–238, 1977.

D. L. Donoho, I. M. Johnstone, G. Kerkyacharian, and D. Picard, “Wavelet Shrinkage: Asymptopia?” Journal of the Royal Statistical Society. Series B (Methodological), vol. 57, no. 2, pp. 301–369, 1995. [Online]. Available: http://statweb.stanford.edu/ imj/WEBLIST/1995/asymp.pdf

X. Zong, “De-noising and contrast enhancement via wavelet shrinkage and nonlinear adaptive gain,” Proceedings of SPIE, vol. 2762, pp. 566–574, 1996. [Online]. Available: http://link.aip.org/link/?PSI/2762/566/1&Agg=doi

C. Taswell, “The What How, and Why of Wavelet Shrinkage Denoising,” Computing in Science and Engineering, vol. 2, no. 3, pp. 12-19, 2000.


Refbacks

  • There are currently no refbacks.


 

 
  

 

  


About IJC | Privacy PolicyTerms & Conditions | Contact Us | DisclaimerFAQs 

IJC is published by (GSSRR).