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Image Super-Resolution Using Deep Convolutional Networks in Tensorflow https://arxiv.org/abs/1501.00092v3
| Date | Stars |
|---|---|
| 2026-07-24 | 614 |
| 2026-07-25 | 614 |
| 2026-07-28 | 614 |
| 2026-07-30 | 614 |
| 2026-08-06 | 614 |
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# SRCNN-Tensorflow
Tensorflow implementation of Convolutional Neural Networks for super-resolution. The original Matlab and Caffe from official website can be found [here](http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html).
## Prerequisites
* Tensorflow
* Scipy version > 0.18 ('mode' option from scipy.misc.imread function)
* h5py
* matplotlib
This code requires Tensorflow. Also scipy is used instead of Matlab or OpenCV. Especially, installing OpenCV at Linux is sort of complicated. So, with reproducing this paper, I used scipy instead. For more imformation about scipy, click [here](https://www.scipy.org/).
## Usage
For training, `python main.py`
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For testing, `python main.py --is_train False --stride 21`
## Result
After training 15,000 epochs, I got similar super-resolved image to reference paper. Training time takes 12 hours 16 minutes and 1.41 seconds. My desktop performance is Intel I7-6700 CPU, GTX970, and 16GB RAM. Result images are shown below.<br><br>
Original butterfly image:
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Bicubic interpolated image:
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Super-resolved image:
## References
* [liliumao/Tensorflow-srcnn](https://github.com/liliumao/Tensorflow-srcnn)
* - I referred to this repository which is same implementation using Matlab code and Caffe model.
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* [carpedm20/DCGAN-tensorflow](https://github.com/carpedm20/DCGAN-tensorflow)
* - I have followed and learned training process and structure of this repository.
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matched fp:3872db0e219ef129, topic:super-resolution, desc:super resolution, readme:super resolution
matched fp:3872db0e219ef129, topic:tensorflow
matched fp:3872db0e219ef129, topic:computer-vision