  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.
