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Found 3 papers, 1 papers with code
Self-Supervised Blind Motion Deblurring With Deep Expectation Maximization
In contrast, this paper presents a dataset-free deep learning method for removing uniform and non-uniform blur effects from images of static scenes.
Deep Learning for Handling Kernel/model Uncertainty in Image Deconvolution
It is known that non-blind deconvolution is susceptible to such a kernel/model error.
Variational-EM-Based Deep Learning for Noise-Blind Image Deblurring
Based on the framework of variational expectation maximization (EM), an iterative noise-blind deblurring scheme is proposed which integrates the estimation of noise level and the quantification of image prior uncertainty.
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