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.