Reconfigurable Wavelet Thresholding for Image Denoising while Keeping Edge Detection

This paper proposes an reconfigurable adaptive threshold estimation method for image denoising in the wavelet domain based on the generalized Guassian distribution (GGD) modeling of sub-band coefficients. The proposed method called RegularShrink is computationally more efficient and adaptive because the parameters required for estimating the threshold depend on sub-band data Edge information is the most important high frequency information of an image, so we should try to maintain more edge information while denoising. In order to preserve image details as well as canceling image noise, we present a new image denoising method: image denoising based on edge detection. Before denoising, image’s edges are first detected, and then the noised image is divided into two parts: edge part and smooth part. We can therefore set high denoising threshold to smooth part of the image and low Denoising threshold to edge part. The theoretical analyzes and experimental results presented in this paper show that, compared to commonly used wavelet threshold denoising methods, the proposed algorithm could not only keep edge information of an image, but also could improve signal-to-noise ratio of the denoised image.