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From Image Restoration to Compressive Sampling in Computational Photography. A Bayesian Perspective.
Published on Feb 4, 20255321 Views
the quality of the imaging system or reproducing the scene conditions in order to acquire another image is not an option, computational approaches provide a powerful means for the recovery of lost inf
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Presentation
From Image Restoration to Compressive Sampling in Computational Photography. A Bayesian Perspective00:00
Outline00:22
Let’s go back some 25 years00:58
Observation Process p(y|x)01:44
Inference03:40
Observation / Restoration04:52
Image Restoration - 105:57
Image Restoration - 209:23
Image Restoration - 311:04
Image Restoration - 412:02
Image Restoration - 513:16
Image Restoration - 616:10
Image Restoration - 716:33
Blind Deconvolution18:03
Taking pairs of images - 120:26
Taking pairs of images - 221:23
Taking pairs of images - 322:15
Taking pairs of images - 422:31
Taking pairs of images - 524:37
Modifying the aperture - 124:43
Modifying the aperture - 225:11
Capturing the Light Field using CS - 125:22
Capturing the Light Field using CS - 225:42
Capturing the Light Field using CS - 325:46
Capturing the Light Field using CS - 425:57
Capturing the Light Field using CS - 526:21
Capturing the Light Field using CS - 626:37
Capturing the Light Field using CS - 726:44
Our prototype - 127:15
Our prototype - 227:48
Our prototype - 327:51
Our prototype - 428:01
Our prototype - 528:03
Our prototype - 628:16
Our prototype - 728:32
Our prototype - 828:35
Our prototype - 928:48
Our prototype - 1028:57
Our prototype - 1129:17
Our prototype - 1229:22
Our prototype - 1329:38
Our prototype - 1429:45
Our prototype - 1529:49
Our prototype - 1629:57
Our prototype - 1730:02
Collaborators - 130:05
Collaborators - 230:26