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Blind deconvolution is a challenging problem, but in low-light it is evenmore difficult. Existing algorithms, both classical and deep-learning based,are not designed for this condition. When the photon shot noise is strong,conventional deconvolution methods fail because (1) the image does not haveenough signal-to-noise ratio to perform the blur estimation; (2) While deepneural networks are powerful, many of them do not consider the forward process.When the noise is strong, these networks fail to simultaneously deblur anddenoise; (3) While iterative schemes are known to be robust in the classicalframeworks, they are seldom considered in deep neural networks because itrequires a differentiable non-blind solver.  This paper addresses the above challenges by presenting an\emph{unsupervised} blind deconvolution method. At the core of this method is areformulation of the general blind deconvolution framework from theconventional image-kernel alternating minimization to a purely kernel-basedminimization. This kernel-based minimization leads to a new iterative schemethat backpropagates an unsupervised loss through a pre-trained non-blind solverto update the blur kernel. Experimental results show that the proposedframework achieves superior results than state-of-the-art blind deconvolutionalgorithms in low-light conditions.

Photon-Limited Blind Deconvolution using Unsupervised Iterative Kernel Estimation