Improved Image Denoising Technique using Neighboring Wavelet Coefficients of Optimal Wavelet with Adaptive Thresholding
The optimal choice of the wavelet and thresholding function has restricted the wide spread use of wavelet transform in image denoising application. The aim of this paper is twofold; firstly to suggest some new thresholding method for image denoising in the wavelet domain by keeping into consideration the shortcomings of conventional methods and secondly to explore the optimal wavelet for image denoising. In this paper we proposed a computationally more efficient thresholding scheme by incorporating the neighbouring wavelet coefficients, with different threshold value for different sub bands and it is based on generalized Gaussian Distribution (GGD) modeling of sub band coefficients. In this proposed method, the choice of the threshold estimation is carried out by analyzing the statistical parameters of the wavelet sub band coefficients. It is demonstrated that our proposed method performs better than: VisuShrink, Normalshrink and NeighShrink algorithms in terms of PSNR ratio. Further a comparative analysis has been made between Daubechies, Haar, Symiet and Coiflet wavelets to explore the optimum wavelet for image denoising with respect to Lena image. It has been found that with Coiflet wavelet higher PSNR ratio is achieved than others. Hence proposed for denoising the Lena image.
Image denoising Gaussian Noise Thresholding Neighbouring coefficients Wavelet
Rakesh Kumar B. S. Saini
B R Ambedkar National Institute of Technology Jalandhar-144011, India
国际会议
2011 International Conference on Database and Data Mining(ICDDM 2011)(2011年数据库和数据挖掘国际会议)
三亚
英文
143-147
2011-03-25(万方平台首次上网日期,不代表论文的发表时间)