LAUSR

Abstract Blind image deblurring is a challenging low-level vision problem which aims to restore a sharp image only from the blurry observation. Few known information makes this problem fundamentally ill-posed. Most recent works focus on designing various priors on both latent image and blur kernel based on the maximum a posteriori (MAP) model to restrict the solution space. However, their performance is highly related to these hand-crafted explicit priors. In fact, the pre-designed explicit priors may have less flexibility to fit different image structures in real-world scenarios. To overcome these difficulties, we propose a novel framework, named Hybrid Deep Priors Model (HDPM), to simulate the propagation of sharp latent image used in kernel estimation and final deconvolution. Specifically, we introduce the learnable implicit deep prior and hand-crafted explicit prior as regularizations into the MAP inference process to extract the detailed texture and sharp structures of latent image, respectively. In HDPM, we can successfully take the advantages of explicit cues based on task information and implicit deep priors from training data to facilitate the propagation of sharp latent image which is beneficial for the kernel estimation. Extensive experiments demonstrate that the proposed method performs favorably against the state-of-the-art deblurring methods on benchmarks, challenging scenarios and non-uniform images.