Super_Resolution_DNN

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Super_Resolution_DNN

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Implementation of Deep Learning Neural Network (RUnet) for Super-Resolution

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# Super-Resolution Using Deep Convolutional Networks

[![forthebadge](https://forthebadge.com/images/badges/made-with-python.svg)](https://www.python.org/)

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com)
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This is a project about Neural Networks for Data Science course implementing a state-of-the-art architecture from a given scientific paper (see [1]). More specifically, we will construct the Robust-UNet architecture aiming to improve the resolution of an input images using a particular dataset.

![RUNet Architecture](https://raw.githubusercontent.com/cerniello/Super_Resolution_DNN/master/img/00_RUnet.png)

## Results:

Below some of our results:

![](https://raw.githubusercontent.com/cerniello/Super_Resolution_DNN/master/img/results_1.png)
![](https://raw.githubusercontent.com/cerniello/Super_Resolution_DNN/master/img/results_2.png)
![](https://raw.githubusercontent.com/cerniello/Super_Resolution_DNN/master/img/results_3.png)

## References:

[[1] Hu, Xiaodan, et al. "RUNet: A Robust UNet Architecture for Image Super-Resolution." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2019](http://openaccess.thecvf.com/content_CVPRW_2019/html/WiCV/Hu_RUNet_A_Robust_UNet_Architecture_for_Image_Super-Resolution_CVPRW_2019_paper.html)

## Authors:

Francesco Russo, Michele Cernigliaro, Iason Tsardanidis

![](https://raw.githubusercontent.com/cerniello/Super_Resolution_DNN/master/img/tre_moschettieri.jpg)

Super_Resolution_DNN

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