Regularization-Based Semi-Blind Image Deconvolution Using an Improved Function for PMMW Images Application

Abstract

Image deconvolution is a method for reversing the distortion in an imaging system. It is widely used in removing blur and noise from a degraded image. This is an ill-posed inverse problem, and one should use regularization techniques to solve this problem. Regularization functions play an important role in finding the desired solution. In this paper, a new function in wavelet domain is proposed, which is useful in blind deconvolution. In addition, a simple algorithm is used to obtain the restored image and unknown point spread function. The proposed approach is tested on three standard images and then compared with the previous methods using standard metrics. Real Passive Millimeter Wave (PMMW) images are also used to obtain the sharp deblurred images. Simulation results show that the proposed method can improve the quality of the restored image.