Multi-Scale Feature Fusion with Adaptive Attention for Robust Image Deblurring

Deblurring images is still one of the most significant issues in computer vision and image processing, especially when handling complex blur patterns across different spatial contexts. This paper introduces a novel Multi-Scale Feature Fusion with Adaptive Attention (MSFAA) framework for image deblurring that effectively addresses varying degrees of blur in different image regions. Our approach introduces an adaptive attention mechanism that dynamically weighs multi-scale feature representations based on local blur characteristics. We propose a novel Context-Aware Feature Importance (CAFI) module that directs the network’s attention to computational resources on the most difficult regions while efficiently processing easier areas. Extensive experiments on benchmark datasets demonstrate that our approach achieves state-of-the-art performance, with particularly significant improvements on images containing spatially-varying blur. The proposed method reduces computational complexity by $18 \%$ compared to current leading approaches while improving deblurring quality metrics by an average of 2.1dB PSNR and 0.043 SSIM. Our method also demonstrates superior visual quality and preservation of fine details in challenging real-world scenarios.