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.

翻译：在从显微镜到天文学的各个领域,盲人的分解是一个弊端问题。问题的性质不当,需要适当的先入为主,才能达成理想的解决方案。最近,人们已经表明,深层次的学习结构可以在未受监督的盲人分解优化期间作为图像生成,但即使在单一图像上也经常出现性能波动。我们提议在优化期间使用维纳分解来引导图像生成器,通过使用从高山开始的辅助内核估计来提供模糊图像的精细版本。我们观察到,与低频率特征相比,高频分解的元件的复制延迟。此外,图像生成器复制的分解图像的低频率特征比模糊图像的更快。我们将计算过程嵌入一个受限制的优化框架,并表明拟议方法在多个数据集之间产生更高的稳定性和性能。此外,我们提供了代码。