  Blind deconvolution is an ill-posed problem arising in various fields ranging
from microscopy to astronomy. The ill-posed nature of the problem requires
adequate priors to arrive to a desirable solution. Recently, it has been shown
that deep learning architectures can serve as an image generation prior during
unsupervised blind deconvolution optimization, however often exhibiting a
performance fluctuation even on a single image. We propose to use
Wiener-deconvolution to guide the image generator during optimization by
providing it a sharpened version of the blurry image using an auxiliary kernel
estimate starting from a Gaussian. We observe that the high-frequency artifacts
of deconvolution are reproduced with a delay compared to low-frequency
features. In addition, the image generator reproduces low-frequency features of
the deconvolved image faster than that of a blurry image. We embed the
computational process in a constrained optimization framework and show that the
proposed method yields higher stability and performance across multiple
datasets. In addition, we provide the code.
