Nearest Neighbor Classification for Classical Image Upsampling
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909287253540864 |
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| author | Matthews, Evan Prate, Nicolas |
| author_facet | Matthews, Evan Prate, Nicolas |
| contents | Given a set of ordered pixel data in the form of an image, our goal is to perform upsampling on the data such that: the resulting resolution is improved by some factor, the final result passes the human test, having added new, believable, and realistic information and detail to the image, the time complexity for upscaling is relatively close to that of lossy upscaling implementations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19611 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Nearest Neighbor Classification for Classical Image Upsampling Matthews, Evan Prate, Nicolas Computer Vision and Pattern Recognition Graphics Given a set of ordered pixel data in the form of an image, our goal is to perform upsampling on the data such that: the resulting resolution is improved by some factor, the final result passes the human test, having added new, believable, and realistic information and detail to the image, the time complexity for upscaling is relatively close to that of lossy upscaling implementations. |
| title | Nearest Neighbor Classification for Classical Image Upsampling |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2403.19611 |