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Project description

# theanonSR
Super Resolution using Deep Convolutional Neural Network(SRCNN) using theano

## Introduction
theanonSR upscales photo image to x2 size.

Original image


Upscaled image using python OpenCV library


**Upscaled image using theanonSR**


## Description

It is developed on python using theano library.

This project is to understand/study how deep convolutional neural network works
to learn super resolution of the image.

[TODO] Currently GPU support is not implemented yet.

## References
Originally, I was inspired this project from waifu2x project, which uses Torch7 to implement SRCNN.

- Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, "Image Super-Resolution Using Deep Convolutional Networks",

SRCNN, super resolution using deep convolutional neural network, is introduced in this paper.

- [waifu2x](

It is the popular project for image super resolution for Anime-Style art.
It also has a good performance.

- [theano](

Machine learning library which can be written in python.
It also provides nice
[Deep Learning Tutorials]( to study how to implement deep neural network.

## How to use

### Basic usage
Just specify image file path which you want to upscale.

Ex. Upscaling input.jpg
python code/ input.jpg

### Specify output file name and path
Ex. Upscaling /path/to/input.jpg to /path/to/output.jpg
python code/ /path/to/input.jpg /path/to/output.jpg

### Specify model to use:
You can specify directory name in the /model directory, as the model.

Ex. use model 32x3x3_32x3x3_32x3x3_1x3x3,
python code/ -m 32x3x3_32x3x3_32x3x3_1x3x3 input.jpg

## Training

You can construct your own convolutional neural network, and train it easily!

### 1. Data preparation
Put training images[1] inside data/training_images directory.
(I used 2000 photo images during the training.)

[1]: Currently, image must be more than or equal to the size 232 x 232.

### 2. Construct your model (convolutional neural network)
Open code/tools/, and modify this code to construct your own model.
Then execute it.
python code/tools/

It will generate train.json file for your own model at model/your_model directory.

### 3. Training the model
Once prepared your own model to be trained, you can train your model by
python code/ -m your_own_model
``` refers model/your_own_model/train.json to construct CNN (Convolutional Neural Network)
for training.

## Contribution is welcome

The performance of SR for this project is not matured.
You are welcome to improve & contribute this project.
If you could get any model which performs better performance, feel free to send me a pull request!

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