FR-SRGAN

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Class Final Project for MIT 6.819 Advances in Computer Vision

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# FR-SRGAN

Final Project for MIT 6.819 Advances in Computer Vision

## Required Package

```
pytorch
skimage
numpy
cv2
tqdm
```

## Usage

TODO

## Overview

This project is based on the following two papers.

> Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network 
>
> https://arxiv.org/abs/1609.04802
>
> Frame-Recurrent Video Super-Resolution 
>
> https://arxiv.org/abs/1801.04590

We are trying to combine these two models together in our project (this is the where the name come from "Frame Recurrent Super Resolution GAN").

Our final report is available here:
[Report](https://drive.google.com/file/d/19NxecT98x1ULJFUWGGqmW4E2dadhvC0D/view?usp=sharing)

## Dataset

We used the dataset from [toflow-dataset](http://data.csail.mit.edu/tofu/testset/vimeo_test_clean.zip), which is about 15GB, containing 7.8k video clips with 7 frames per clip.

## Results

We trained our model on the dataset for 9 epochs. and compared with default SRGAN model in the repo. **We did not retrain SRGAN in our dataset**, so the result is for reference only.

The following results are produced by 7-epoch model.

### Temporal Profile

#### SRGAN
[![Frvri4.png](https://s1.ax1x.com/2018/12/21/Frvri4.png)](https://imgchr.com/i/Frvri4)

#### FRVSR
[![FrvyW9.png](https://s1.ax1x.com/2018/12/21/FrvyW9.png)](https://imgchr.com/i/FrvyW9)

#### FR-SRGAN
[![Frv6zR.png](https://s1.ax1x.com/2018/12/21/Frv6zR.png)](https://imgchr.com/i/Frv6zR)

#### Ground Truth
[![FrvsJJ.png](https://s1.ax1x.com/2018/12/21/FrvsJJ.png)](https://imgchr.com/i/FrvsJJ)

### Comparison
[![Frxiyq.gif](https://s1.ax1x.com/2018/12/21/Frxiyq.gif)](https://imgchr.com/i/Frxiyq)

## Acknowledgement

We referred to [SRGAN Implementation](https://github.com/leftthomas/SRGAN) by LeftThomas in our project. We would like to thank the author for his fabulous work.

FR-SRGAN

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