Image denoising in the wavelet domain using a new adaptive thresholding function

Abstract

In this paper, a new thresholding function is proposed for image denoising in the wavelet domain. The proposed function is further used in a new subband-adaptive thresholding neural network to improve the efficiency of the denoising procedure. Some new adaptive learning types are also proposed. In these learning methods, the threshold and the thresholding function effects are considered simultaneously. These methods are used to suppress two types of important noises, Gaussian and speckle, ranging from natural images to ultrasound and SAR pictures. The simulation results show that the proposed thresholding function has superior features compared to conventional methods when used with the proposed adaptive learning types. This makes it an efficient method in image denoising applications.