Blind Image Deblurring with Unknown Kernel Size and Substantial Noise

Zhong Zhuang, Taihui Li, Hengkang Wang, Ju Sun

Keywords: Blind Deconvolution, Practical, Single-Instance Deep Learning

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.

Submission Number: 31