Gradient domain model-driven algorithm unfolding network for blind image deblurring

Blind image deblurring remains a challenging ill-posed problem due to the simultaneous estimation of clear images and blur kernels. Recently, image deblurring methods that utilize algorithm unfolding techniques have made significant advancements. However, the classical image gradient prior, despite its effectiveness, remains an unexplored avenue in deep unfolding frameworks. In this paper, to further exploit the role of image gradients in deblurring tasks, we introduce a gradient-driven algorithm unfolding network (GDUNet) by generalizing the classical sparse gradient deblurring model. We design specific proximal mapping modules for various prior terms to flexibly learn more accurate prior distributions. The entire framework is structured around alternating updates of images and kernels, naturally embedding the convolutional paradigm of blurred images. Additionally, we introduce a blur pattern attention module (BPAM) designed to modulate the finest-scale image features and facilitate the restoration of the blur kernel. Experimental results on multiple color-blurred image datasets indicate that our GDUNet achieves superior performance compared to state-of-the-art methods. The code is available at https://github.com/Redamancy0222/GDUNet.