Email this article 
Open Access
Subscription or Fee Access
Video Denoising Based on SWT & DWT Implemented with Soft Shrinkage Rule
Abstract
Video denoising is an important step in image and
video processing applications. Digital images are corrupted by
various types of noise during acquisition and transmission. In this paper, an approach for video / image frame denoising based on Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT) for Additive White Gaussian Noise (AWGN)removal is proposed. The noise is minimized by both hard and soft thresholding of high frequency sub-bands of SWT and DWT,followed by bicubic interpolation of high frequency sub-bands of DWT and SWT. This result in modified noise free higher order subbands.The results obtained on wide range of noise corruption (up to 40%) are shown and discussed. Moreover, comparison with wellestablished
methods for AWGN removal is also provided. Obtained
results reveal that the proposed algorithm outperforms other
approaches of AWGN removal and its performance is close to
optimality. The proposed algorithm can significantly improve the visual quality of the noisy video / image frame by maintaining sharp edges, and clearly smooth out noise from most parts of the image frame.
Keywords
References
Kai Zeng and Zhou Wang, “Polyview Fusion: A Strategy to Enhance
Video Denoising Algorithms”, IEEE Transaction on Image Processing,
Vol. 1, Oct 2011, pp. 1-7.
Qing Xu, Hailin Jiang and Riccardo Scopigno, “A New Approach For
Very Dark Video Denoising And Enhancement” 17th IEEE International
Conference on Image Processing (ICIP), Sept 2010, pp. 1185 – 1188.
Jingjing Dai, Oscar C. Au, Wen Yang and Chao Pang, “Color Video
Denoising Based on Adaptive color Space Conversion”, IEEE
International Symposium on Circuits and Systems (ISCAS),
Proceedings, June 2010, pp. 2992 – 2995
Aditya Acharya and Sukadev Meher, “Robust Video Denoising for
Better Subjective Evaluation”, IEEE International Conference on Image
Information Processing (ICIIP), Nov 2011, pp. 1-5.
Yan Chen, Student Member, IEEE, Oscar C. Au, “Simultaneous MAPBased
Video Denoising and Rate-Distortion Optimized Video
Encoding” IEEE Transactions on Circuits and Systems for Video
Technology, Jan 2009, pp. 15 – 26.
Jingyu Yang, Yao Wang, Fellow and Wenli Xu, “Image and Video
Denoising Using Adaptive Dual-Tree Discrete Wavelet Packets”, IEEE
Transactions on Circuits and Systems for Video Technology, May 2009,
pp. 642 – 655.
Gijesh Varghese and Zhou Wang, “Video Denoising Based on a
Spatiotemporal Gaussian Scale Mixture Model”, IEEE Transactions on
Circuits and Systems for Video Technology, July 2010, pp. 1032 – 1040.
Jingjing Dai, Oscar C. Au, Chao Pang and Feng Zou, “Video Denoising
Based On Transform Domain Minimum Mean Square Error”, 18th IEEE
International Conference on Image Processing (ICIP), Sept 2011, pp.
– 2576.
Florian Luisier and Thierry Blu, “SURE-LET for Orthonormal Wavelet-
Domain Video Denoising”, IEEE Transactions on Circuits and Systems
for Video Technology, June 2010, pp. 913 – 919.
Ming Yu, Xiaohong Tian and Yingchun Guo, “A Video Denoising
Algorithm in Wavelet Domain”, Second International Conference on
Intelligent Networks and Intelligent Systems, Nov 2009, pp. 298 – 301.
A. Pizurica, V. Zlokolica, and W. Philips, “Noise reduction in video
sequences using wavelet-domain and temporal filtering,” in Proceeding.
Soc. Photo- Optic. Instrumentation Eng. Conference Wavelet
Application Ind. Process. , Vol. 5266. Feb. 2004, pp. 48–59.
V.R.Vijaykumar, P.T.Vanathi, P.Kanagasabapathy and D.Ebenezer,
“Robust Statistics Based Algorithm to Remove Salt and Pepper Noise in
Images”, International Journal of Signal Processing, 2009, pp-164-173.
K.Dabov, A.Foi and K.Egiazarian, “Video denoising by sparse 3-D
transform domain collaborative filtering”, in proc. Eur. Signal process.
Conf (EUSIPCO), Poznan, Poland, Sep.2007, pp. 1257-1260.
M.Protter and M.Elad, “Image sequence denoising via sparse and
redundant representation”, IEEE Tran. Image process. Vol 18, No 1 pp.
-35, Jan 2009.
Z.Wang and Q.Li, “Statistics of natural image sequences: Temporal
motion smoothness by local phase correlations”, in proc. Human vision
Electron. Image. IX, SPIE, Vol 7240. Jan 2009, pp. 72400W-172400W-
R.Vijaya arjunan and V.Vijaya kumar, “Medical Image Denoising based
on Multi resolution Analysis using wavelet”, International Journal of
Digital Image Processing, Vol 4, No 1, January 2012. pp. 44-48.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.