A Novel Image Denoising using Matched Partial Biorthogonal Wavelets and Adaptive Thresholding

Abstract

The denoising of a natural image corrupted by Gaussian noise is a problem in signal or image processing Even though much work has been done in the field of wavelet thresholding, most of it was focused on statistical modeling of wavelet coefficients and the optimal choice of thresholds. This paper describes a new method for suppression of noise in image by fusing the wavelet Denoising technique with optimized thresholding function, improving the denoised results significantly by using partial biorthogonal wavelets. In today's scenario denoising techniques use the classical orthonormal wavelets for decomposition of an image corrupted with additive white Gaussian noise, upon which various thresholding strategies are built. We present a method to design partial biorthogonal wavelet bases and report on their potential for denoising. This paper describes a new method for suppression of noise in image by fusing the wavelet Denoising technique with optimized thresholding function, improving the denoised results significantly. Simulated noise images are used to evaluate the denoising performance of proposed algorithm along with another wavelet-based denoising algorithm. Experimental result shows that the proposed denoising method outperforms standard wavelet denoising techniques in terms of the PSNR and the preservation of edge information. The use of available biorthogonal wavelets in image denoising is less common because of their poor performance. But when we combine the approach of fusion with partial biorthogonal wavelets then the performance is increases in comparison of traditional methodology. This point to the importance of matching when using wavelet-based denoising.