Blind Image Deblurring with Unknown Kernel Size and Substantial Noise

Blind image deblurring (BID) has been extensively studied in computer visionand adjacent fields. Modern methods for BID can be grouped into two categories:single-instance methods that deal with individual instances using statistical infer-ence and numerical optimization, and data-driven methods that train deep-learningmodels to deblur future instances directly. Data-driven methods can be free fromthe difficulty in deriving accurate blur models, but are fundamentally limited bythe diversity and quality of the training data—collecting sufficiently expressiveand realistic training data is a standing challenge. In this paper, we focus onsingle-instance methods that remain competitive and indispensable, and address thechallenging setting unknown kernel size and substantial noise, failing state-of-the-art (SOTA) methods. We propose a practical BID method that is stable againstboth, the first of its kind. Also, we show that our method, a non-data-drivenmethod, can perform on par with SOTA data-driven methods on similar data thelatter are trained on, and can perform consistently better on novel data.