Low dimensional manifold model for image processing
53 mins 14 secs,
774.45 MB,
MPEG-4 Video
640x360,
29.97 fps,
44100 Hz,
1.93 Mbits/sec
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About this item
| Description: |
Shi, Z
Wednesday 6th September 2017 - 14:50 to 15:40 |
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| Created: | 2017-09-07 13:58 |
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| Collection: | Variational methods and effective algorithms for imaging and vision |
| Publisher: | Isaac Newton Institute |
| Copyright: | Shi, Z |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | In this talk, I will introduce a novel low dimensional manifold model for image processing problem.
This model is based on the observation that for many natural images, the patch manifold usually has low dimension structure. Then, we use the dimension of the patch manifold as a regularization to recover the original image. Using some formula in differential geometry, this problem is reduced to solve Laplace-Beltrami equation on manifold. The Laplace-Beltrami equation is solved by the point integral method. Numerical tests show that this method gives very good results in image inpainting, denoising and super-resolution problem. This is joint work with Stanley Osher and Wei Zhu. |
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