Digital image restoration: blur as a chance and not a nuisance
Filip Sroubek
(Institute of Information Theory and Automation, Czech Republic)
We rely on images with ever growing emphasis. Our perception of the
world is however limited by imperfect measuring conditions and devices
used to acquire images. By image restoration, we understand
mathematical procedures removing degradation from images. Two prominent
topics of image restoration that has evolved considerably in the last
10 years are blind deconvolution and superresolution. Deconvolution by
itself is an ill-posed inverse problem and one of the fundamental
topics of image processing. The blind case, when the blur kernel is
also unknown, is even more challenging and requires special
optimization approaches to converge to the correct solution.
Superresolution extends blind deconvolution by recovering lost spatial
resolution of images.
In this talk we will cover the recent advances in
both topics that pave the way from theory to practice. Various real
acquisition scenarios will be discussed together with proposed
solutions for both blind deconvolution and superresolution and
efficient numerical optimization methods, which allow fast
implementation. Finally, we will illustrate that combing deblurring
with tracking leads to interesting applications in sports videos