Wavelet based Image Denoising using Weighted Highpass Filtering Coefficients and Adaptive Wiener Filter

An efficient method of removing noise from the image while preserving edges and other details is a great challenge for researcher. Image denoising refers to the task of recovering a good estimate of the true image from the degraded image without altering and changing useful structure in the image such as discontinuities and edges. Various algorithm has been developed in past for image denoising but still it has scope for improvement. In this paper, we introduced an intelligent iterative noise variance estimation system which denoised the noisy image. Proposed algorithm is based on wavelet transform that denoised the noisy image by adding weighted highpass filtering coefficients in wavelet domain that is the novelty of the proposed work. Thereafter denoised algorithm further enhanced by adaptive wiener filter in order to achieve the maximum PSNR. Experimental results show that the proposed algorithm improves the denoising performance measured in terms of performance parameter and gives better visual quality. Mean Square Error (MSE), Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR) used as a performance parameters which measure the quality of an image.