Beyond Uniformity: Deblurring Images With Complex Noise Patterns Using Half Quadratic Splitting

Abstract

Image deblurring is a critical area of research, traditionally focused on scenarios where blurred images are formed by convolving a clean image with a blur kernel and adding white Gaussian noise. However, real-world images often suffer from non-uniform and non-stationary noise, which presents additional challenges that current methods struggle to address effectively. In this paper, we investigate the performance of the state-of-the-art techniques for handling non-uniform noise. Our simulation results show that these methods are not designed to handle the complexities associated with non-stationary noise. To overcome these limitations, we propose a novel technique that utilizes the estimated noise standard deviation map for better reconstruction. Through simulation results, we demonstrate that our proposed method significantly outperforms the state-of-the-art image deblurring methods in the presence of non-stationary noise.