Enhancing defocus deblurring via residual fusion and multi-scale transposed attention

Abstract Defocus image deblurring aims to reconstruct sharp images from defocused inputs. Although deep learning-based methods have achieved significant progress, existing algorithms still struggle to effectively extract critical details from highly redundant feature representations and to achieve clear structural and textural reconstruction in complex texture regions. To address these issues, we propose a novel W-shaped network architecture that departs from the conventional U-shaped paradigm.