Iterative Blind Restoration Algorithm Based on Spatial and Frequency Domains

In this paper, we introduce an iterative blind restoration algorithm that operates in both spatial and frequency domains, representing a significant research focus in the field of image blind restoration. The algorithm utilizes spatial information (such as local pixel correlations and edge structures) and frequency-domain characteristics (including spectral decomposition and noise distribution patterns) through an iterative optimization process to progressively reconstruct the original image. Our research and experiments demonstrate that this approach achieves remarkable performance in effectively restoring blurred images. The implementation typically involves alternating between spatial-domain constraints (e.g., using regularization techniques for edge preservation) and frequency-domain filtering operations (such as Wiener filtering or Richardson-Lucy deconvolution variants), with convergence criteria monitoring image quality improvement between iterations. Additionally, we discuss the algorithm's limitations, including sensitivity to initial point spread function estimates and computational complexity challenges, along with potential improvements like adaptive parameter tuning and hybrid optimization strategies. These insights aim to provide valuable references and inspiration for further research in related domains.