Blind image deblurring is the process of deriving a sharp image and a blur
kernel from a blurred image. Blurry images are typically modeled as the
convolution of a sharp image with a blur kernel, necessitating the estimation
of the unknown blur kernel to perform blind image deblurring effectively.
Existing approaches primarily focus on domain-specific features of images, such
as salient edges, dark channels, and light streaks. These features serve as
probabilistic priors to enhance the estimation of the blur kernel. For improved
generality, we propose a novel prior (ReLU sparsity prior) that estimates blur
kernel effectively across all distributions of images (natural, facial, text,
low-light, saturated etc). Our approach demonstrates superior efficiency, with
inference times up to three times faster, while maintaining high accuracy in
PSNR, SSIM, and error ratio metrics. We also observe noticeable improvement in
the performance of the state-of-the-art architectures (in terms of
aforementioned metrics) in deep learning based approaches when our method is
used as a post-processing unit.

图像盲去模糊是从模糊图像中提取出清晰图像和模糊核的过程。本研究提出了一种新的先验（ReLU 稀疏性先验），能够在各种图像分布（自然图像、人脸图像、文本图像、低光图像、饱和图像等）上有效地估计模糊核。我们的方法在 PSNR、SSIM 和错误比等度量指标上保持高准确性的同时，推断时间比现有方法快三倍。当将我们的方法用作后处理单元时，可观察到在基于深度学习的方法中最先进的架构性能上的显著改进。