├── LICENSE
├── README.md
├── code
└── main.ipynb
├── data
└── butterfly.png
├── presentation
├── img
│ ├── mfsr_mrf.gif
│ ├── mfsr_tv.gif
│ ├── readme_img_1.jpg
│ └── readme_img_2.jpg
└── main.pdf
├── report
└── report.pdf
└── results
├── LR
├── 1.jpg
├── 10.jpg
├── 11.jpg
├── 12.jpg
├── 13.jpg
├── 14.jpg
├── 15.jpg
├── 16.jpg
├── 2.jpg
├── 3.jpg
├── 4.jpg
├── 5.jpg
├── 6.jpg
├── 7.jpg
├── 8.jpg
└── 9.jpg
├── estimated_angles.jpg
├── estimated_shifts.jpg
├── hr.jpg
├── loss_hr_estimation.jpg
├── loss_reg_estimation.jpg
├── patch.jpg
├── sr_cubic.jpg
├── sr_gaussian_prior.jpg
├── sr_mrf_prior.jpg
└── sr_tv_prior.jpg
/LICENSE:
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1 | MIT License
2 |
3 | Copyright (c) 2021 Harshit Varma
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 |
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/README.md:
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1 |
Bayesian Image Super-Resolution
2 |
3 |
4 |
5 |
6 |
7 | A Bayesian framework for Multi-Frame Image Super-Resolution, based on ["Bayesian Image Super-Resolution" (ME Tipping and CM Bishop, NeurIPS 2003)](https://papers.nips.cc/paper/2315-bayesian-image-super-resolution.pdf).
8 |
9 | It's a two-stage method wherein the image registration parameters are estimated in the first stage, followed by estimating the HR image in the second stage. The registration parameters are estimated using the marginal likelihood, computed using using a Gaussian process prior over the HR image. In the second stage, any relevant prior (eg. MRF, TV) can be used for estimating the HR image.
10 |
11 | For more details, check out:
12 | - [Report](report/report.pdf)
13 | - [Presentation](presentation/main.pdf)
14 |
15 | ## Results Overview
16 |
17 |
18 |
19 |
20 |
21 | ## Visualization of the HR Image Estimation
22 |
23 |
24 |
25 |
26 |
27 | ## Authors
28 | - [Harshit Varma](https://github.com/hrshtv)
29 | - [Gaurav P](https://github.com/gaurav638012)
30 |
31 | ---
32 | *(This was done as a course project for CS736: Medical Image Computing, Spring 2021, IIT Bombay)*
33 |
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/data/butterfly.png:
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