WAVELET THRESHOLDING APPROACH FOR IMAGE DENOISING
Pankaj Hedaoo1 and Swati S Godbole2
1Department of Electronics & Telecommunication Engineering, G. H. Raisoni College of Engineering, Nagpur, India.
2Department of Electronics & Telecommunication Engineering, G. H. Raisoni College of Engineering, Nagpur, India
ABSTRACT
The original image corrupted by Gaussian noise is a long established problem in signal or image processing .This noise is removed by using wavelet thresholding by focused on statistical modelling of wavelet coefficients and the optimal choice of thresholds called as image denoising . For the first part, threshold is driven in a Bayesian technique to use probabilistic model of the image wavelet coefficients that are dependent on the higher order moments of generalized Gaussian distribution (GGD) in image processing applications. The proposed threshold is very simple. Experimental results show that the proposed method is called BayesShrink, is typically within 5% of the MSE of the best soft-thresholding benchmark with the image. It outperforms Donoho and Johnston Sure Shrink. The second part of the paper is attempt to claim on lossy compression can be used for image denoising .thus achieving the image compression & image denoising simultaneously. The parameter is choosing based on a criterion derived from Rissanen’s minimum description length (MDL) principle. Experiments show that this compression & denoise method does indeed remove noise significantly, especially for large noise power.
KEYWORDS
Image denoising, Wavelet Thresholding, Noise categories, Proposed Method.
Original Scource Link : http://airccse.org/journal/nsa/0711ijnsa02.pdf
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