  Blind deconvolution aims to recover an original image from a blurred version
in the case where the blurring kernel is unknown. It has wide applications in
diverse fields such as astronomy, microscopy, and medical imaging. Blind
deconvolution is a challenging ill-posed problem that suffers from significant
non-uniqueness. Solution methods therefore require the integration of
appropriate prior information. Early approaches rely on hand-crafted priors for
the original image and the kernel. Recently, deep learning methods have shown
excellent performance in addressing this challenge. However, most existing
learning methods for blind deconvolution require a paired dataset of original
and blurred images, which is often difficult to obtain. In this paper, we
present a novel unsupervised learning approach named ECALL
(Expectation-CALibrated Learning) that uses separate unpaired collections of
original and blurred images. Key features of the proposed loss function are
cycle consistency involving the kernel and associated reconstruction operator,
and terms that use expectation values of data distributions to obtain
information about the kernel. Numerical results are used to support ECALL.
