Single Image Defocus Deblurring Based on Structural Information Enhancement

Defocus deblurring is an important task in computer vision that aims to bring images back to clarity. Over recent years, both blind defocuse deblurring and non-blind defocuse deblurring methods have made great progress in the single image defocus deblurring task. However, most of existing methods lack direct enhancement of structural information, resulting in artifacts and structural distortion. To address this challenge, we propose a single image defocus deblurring method to improve the image quality of defocus deblurring based on structural information enhancement. Specifically, we introduce a spatial frequency interaction module to enhance the structure information and texture information of the image from both local and global perspectives. This enhancement is achieved through parallel processing of the features in the spatial and frequency domain. Moreover, we introduce a region information guidance module, which meticulously processes the clear and blurred regions independently during the learning process. This approach minimizes the adverse effects on the clear regions, effectively eliminates artifacts and distorted structures, and preserves the authentic structural and textural information of the image. To evaluate the superiority of the proposed method, we implement extensive experiments on three public benchmark datasets, DPDD, RealDOF and LFDOF. The experimental results demonstrate that our method achieves state-of-the-art performance.