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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.11685 |
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| _version_ | 1866909257484468224 |
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| author | Felzenszwalb, Pedro |
| author_facet | Felzenszwalb, Pedro |
| contents | Deconvolution with a box (square wave) is a key operation for super-resolution with pixel-shift cameras. In general convolution with a box is not invertible. However, we can obtain perfect reconstructions of sparse signals using convex optimization. We give a direct proof that improves on the reconstruction bound that follows from previous results. We also show our bound is tight and matches an information theoretic limit. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11685 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deconvolution with a Box Felzenszwalb, Pedro Numerical Analysis Computer Vision and Pattern Recognition Deconvolution with a box (square wave) is a key operation for super-resolution with pixel-shift cameras. In general convolution with a box is not invertible. However, we can obtain perfect reconstructions of sparse signals using convex optimization. We give a direct proof that improves on the reconstruction bound that follows from previous results. We also show our bound is tight and matches an information theoretic limit. |
| title | Deconvolution with a Box |
| topic | Numerical Analysis Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.11685 |