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 segment 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 GoPro-trained NAFNet's PSNR by 0.05, 0.96, and 7.03, respectively. Project page is available at GitHub \href{https://hplqaq.github.io/projects/sam-deblur}{HPLQAQ/SAM-Deblur}.

翻译：图像去模糊是图像修复领域中的关键任务，旨在消除模糊伪影。然而，处理非均匀模糊的挑战导致其成为病态问题，限制了现有去模糊模型的泛化性能。为解决这一问题，我们首次提出将分割一切模型（Segment Anything Model, SAM）的先验知识融入去模糊任务的框架SAM-Deblur。具体而言，SAM-Deblur分为三个阶段：首先，对模糊图像进行预处理，通过SAM获取分割掩码，并提出掩码丢弃训练方法以增强模型鲁棒性；其次，为充分利用SAM生成的结构先验，我们设计了一种专门用于平均SAM分割区域的掩码平均池化（Mask Average Pooling, MAP）单元，该单元作为即插即用组件可无缝集成至现有去模糊网络；最后，将MAP单元生成的融合特征输入去模糊模型，获得清晰图像。在RealBlurJ、ReloBlur和REDS数据集上的实验结果表明，引入我们的方法后，基于GoPro训练的NAFNet的PSNR分别提升了0.05、0.96和7.03。项目页面见GitHub \href{https://hplqaq.github.io/projects/sam-deblur}{HPLQAQ/SAM-Deblur}。