Feature Adaptive Wavelet Shrinkage for Image Denoising

Abstract

In this paper, a new wavelet shrinkage denoising algorithm is presented. The algorithm uses wavelet transform (WT) to extract information about sharp variation in multiresolution images and applies shrinkage function adapting the image features. The shrinkage function depends on energy of neighboring pixels, whereas in standard wavelet methods, the empirical wavelet coefficients shrink pixel by pixel, on the basis of their individual magnitude. Experiments show that wavelet shrinkage algorithm which uses neighboring pixels energy improves the denoising performance and achieves better peak signal to noise ratio compared to other thresholding algorithms. Due to its low complexity, the proposed algorithm is very suitable for hardware implementation