GKMANet: Self-Attention Gaussian Kernel Mixture Network for Defocus Deblurring

The primary goal of defocus deblurring is to restore details in blurred images, enhancing clarity and usability. Despite recent progress, current methods struggle with uneven blur distribution, particularly in distinguishing multi-scale boundaries, causing inaccurate pixel blur estimation and boundary detail loss. To address this, we propose an end-to-end deep learning approach incorporating a self-attention mechanism. Our method employs a Gaussian Kernel Mixture (GKM) model for compact blur kernel representation, then builds upon the Gaussian Kernel Mixture Network (GKMNet) to design a novel Attention-based GKM Network (GKMANet) via iterative fixed-point expansion. GKMANet introduces a scale-recursive architecture with self-attention to estimate mixing coefficients for effective deblurring. Experiments demonstrate significant superiority over existing techniques, especially in scenarios with indistinct blurred/clear boundaries, highlighting stronger robustness and higher restoration accuracy. This provides a reliable solution for detail recovery in defocus deblurring.