Wiener Guided DIP for Unsupervised Blind Image Deconvolution

• URL: http://arxiv.org/abs/2112.10271v1
• Date: Sun, 19 Dec 2021 22:19:13 GMT
• Title: Wiener Guided DIP for Unsupervised Blind Image Deconvolution
• Authors: Gustav Bredell, Ertunc Erdil, Bruno Weber, Ender Konukoglu
• Abstract summary: Blind deconvolution is an ill-posed problem arising in various fields ranging from microscopy to astronomy. Deep learning architectures can serve as an image generation prior during unsupervised blind deconvolution optimization. We propose to use Wiener-deconvolution to guide the image generator during optimization by providing it a sharpened version of the blurry image.
• Score: 10.440495513371747
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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