Uncertainty-Aware Variate Decomposition for Self-supervised Blind Image Deblurring

Blind image deblurring remains challenging due to the ill-posed nature of the traditional blurring function. Although previous supervised methods have achieved great breakthrough with synthetic blurry-sharp image pairs, their generalization ability to real-world blurs is limited by the discrepancy between synthetic and real blurs. To overcome this limitation, unsupervised deblurring methods have been proposed by using natural priors or generative adversarial networks. However, natural priors are vulnerable to random blur artifacts, while generators of generative adversarial networks always produce inaccurate details and unrealistic colors. Consequently, previous methods easily suffer from slow convergence and poor performance. In this work, we propose to formulate the traditional blurring function as the composition of multiple variates, thus allowing us explicitly define characteristics of residual images between blurry and sharp images. We also propose a multi-step self-supervised deblurring framework to address the slow convergence issue. Our framework continuously decomposes and composes input images, thus utilizing the uncertainty of blur artifacts to obtain diverse pseudo blurry-sharp image pairs for self-supervised learning. This framework is more efficient than previous methods, as it does not rely on natural priors or GANs. Extensive comparisons demonstrate that the proposed framework outperforms state-of-the-art unsupervised methods on both dynamic scene, human-aware centric motion, real-world and out-of-focus deblurring datasets. The codes are available at https://github.com/ddghjikle/MM-2023-USDF.