Image deblurring is a critical task in the field of image restoration, aiming
to eliminate blurring artifacts. However, the challenge of addressing
non-uniform blurring leads to an ill-posed problem, which limits the
generalization performance of existing deblurring models. To solve the problem,
we propose a framework SAM-Deblur, integrating prior knowledge from the Segment
Anything Model (SAM) into the deblurring task for the first time. In
particular, SAM-Deblur is divided into three stages. First, We preprocess the
blurred images, obtain image masks via SAM, and propose a mask dropout method
for training to enhance model robustness. Then, to fully leverage the
structural priors generated by SAM, we propose a Mask Average Pooling (MAP)
unit specifically designed to average SAM-generated segmented areas, serving as
a plug-and-play component which can be seamlessly integrated into existing
deblurring networks. Finally, we feed the fused features generated by the MAP
Unit into the deblurring model to obtain a sharp image. Experimental results on
the RealBlurJ, ReloBlur, and REDS datasets reveal that incorporating our
methods improves NAFNet's PSNR by 0.05, 0.96, and 7.03, respectively. Code will
be available at \href{this https URL}{SAM-Deblur}.

提出 SAM-Deblur 框架，首次将 Segment Anything Model（SAM）的先验知识融入图像去模糊任务，通过预处理、生成结构先验和融合特征等三个阶段，在 RealBlurJ、ReloBlur 和 REDS 数据集上改进了 NAFNet 的 PSNR 分别为 0.05、0.96 和 7.03。