Image denoising in wavelet domain using a new thresholding function

Improving quality of noisy images has been an active area of research in many years. It has been shown that wavelet thresholding methods had better results than classic approaches. However estimation of threshold and selection of thresholding function are still the challenging tasks. In this paper, a new thresholding function is proposed for wavelet thresholding. This function is continues and has higher order derivation. Therefore it is suitable for gradient decent learning methods such as thresholding neural network (TNN). This function is used by the TNN and threshold values for wavelet sub-bands are estimated according to least mean square (LMS) algorithm. The experimental results show improvement in noise reduction from images based on visual assessments and PSNR comparing with well-known thresholding functions.