Image enhancement by wavelet-based thresholding neural network with adaptive learning rate

Abstract

A new approach has been proposed to improve the computational performance of denoising in which adaptively defined learning step size has been used for tuning the parameter of the thresholding function of wavelet transform-based thresholding neural network (WT-TNN) methodology. In this approach, steepest gradient-based learning step size of WT-TNN methodology are changed to the proposed adaptively defined learning step size for tuning the parameters of thresholding function. The results of the image enhanced by such adaptive learning step size exhibit the increase in the speed of learning and improved edge preservation feature. Further, the learning time has also become independent of noise level and initial values of learning parameters.