Blind Image Deblurring with Unknown Kernel Size and Substantial Noise

Abstract

Blind image deblurring (BID) has been extensively studied in computer vision and 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-learning models to deblur future instances directly. Data-driven methods can be free from the difficulty in deriving accurate blur models, but are fundamentally limited by the diversity and quality of the training data—collecting sufficiently expressive and realistic training data is a standing challenge. In this paper, we focus on single-instance methods that remain competitive and indispensable, and address the challenging setting unknown kernel size and substantial noise, failing state-of- the-art (SOTA) methods. We propose a practical BID method that is stable against both, the first of its kind. Also, we show that our method, a non-data-driven method, can perform on par with SOTA data-driven methods on similar data the latter are trained on, and can perform consistently better on novel data.