PIE: Perceptual Image Enhancement via Local Manifold Sampling on Pretrained Diffusion Models
| dc.contributor.advisor | Baraniuk, Richard G | en_US |
| dc.creator | Mayer, Paul Michael | en_US |
| dc.date.accessioned | 2024-01-25T15:40:21Z | en_US |
| dc.date.available | 2024-01-25T15:40:21Z | en_US |
| dc.date.created | 2023-12 | en_US |
| dc.date.issued | 2023-12-01 | en_US |
| dc.date.submitted | December 2023 | en_US |
| dc.date.updated | 2024-01-25T15:40:21Z | en_US |
| dc.description.abstract | PIE: Perceptual Image Enhancement via Local Manifold Sampling on Pretrained Diffusion Models | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.citation | Mayer, Paul Michael. "PIE: Perceptual Image Enhancement via Local Manifold Sampling on Pretrained Diffusion Models." (2023). Master's thesis, Rice University. https://hdl.handle.net/1911/115437 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/115437 | en_US |
| dc.language.iso | eng | en_US |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | en_US |
| dc.subject | Image Models | en_US |
| dc.subject | Deep Neural Networks | en_US |
| dc.subject | Generative Models | en_US |
| dc.subject | Diffusion Models | en_US |
| dc.subject | Deep Learning | en_US |
| dc.subject | Image Enhancement | en_US |
| dc.subject | Denoising | en_US |
| dc.title | PIE: Perceptual Image Enhancement via Local Manifold Sampling on Pretrained Diffusion Models | en_US |
| dc.type | Thesis | en_US |
| dc.type.material | Text | en_US |
| thesis.degree.department | Electrical and Computer Engineering | en_US |
| thesis.degree.discipline | Engineering | en_US |
| thesis.degree.grantor | Rice University | en_US |
| thesis.degree.level | Masters | en_US |
| thesis.degree.name | Master of Science | en_US |
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