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Motion blur identification from image gradients
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-642-58216-5_6openalex-snapshot
Maximum Likelihood Identification and Restoration of Images Using the Expectation-Maximization Algorithm
inferredcites →https://doi.org/10.1016/1049-9652(91)90051-kopenalex-snapshot
Restoration of real-world motion-blurred images
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Two motion-blurred images are better than one
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Maximum likelihood parametric blur identification based on a continuous spatial domain model
inferredcites →https://doi.org/10.1109/cvpr.2007.383029openalex-snapshot
Single Image Motion Deblurring Using Transparency
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Simultaneous image formation and motion blur restoration via multiple capture
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Blind deconvolution of spatially invariant image blurs with phase
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Identification of image and blur parameters for the restoration of noncausal blurs
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Vibrated image restoration from a single frame
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A Fast Algorithm for Deblurring Models with Neumann Boundary Conditions
inferredcites →https://doi.org/10.1145/1141911.1141956openalex-snapshot
Removing camera shake from a single photograph
inferredcites →https://doi.org/10.1145/1179352.1141956openalex-snapshot
Removing camera shake from a single photograph
inferredcites →https://doi.org/10.1364/josa.63.001571openalex-snapshot
Determination of optical transfer function by inspection of frequency-domain plot
inferredcites →https://doi.org/10.1364/josaa.15.001512openalex-snapshot
Direct method for restoration of motion-blurred images
inferredcites →https://doi.org/10.7551/mitpress/7503.003.0110openalex-snapshot
Blind Motion Deblurring Using Image Statistics
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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