image-denoising

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image-denoising

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[ICMLA 2016]: Code for the paper "Denoising high resolution images using deep learning approach"

image-denoising 최신버젼 다운로드

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// repository documentation

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# Introduction

This repository contains the code for our work on densoising high resolution images using deep learning [paper](http://ieeexplore.ieee.org/document/7838260/). Present state-of-the-art methods like BM3D, KSVD and Non-local means do produce high quality denoised results. But when the size of image becomes very high, for ex. 4000 x 80000 pixels, those high quality results come at a cost of high computational time. This time consuming factor serves as a motivation to come up with a model that can provide comparable results, if not better, in much less time. So, I've used a deep learning approach that automatically tries to learn the function that maps a noisy image to its denoised version. I've used thenao as the deep learning framework, and have worked on the [publicly available codes](https://github.com/lisa-lab/DeepLearningTutorials) provided by the [MILA Lab](https://mila.umontreal.ca/).

# Data
Unfortunately, the complete data on which I actually trained this model cannot be released publicly, since I used data that belong to ISRO (images captured by CARTOSAT 2), although I have included snippets of one or two images in the results section, to provide a sense of what the data looks like. But anyone can easily use their own data (black and white noisy and denoised images for now) and train the model accordingly.

# Dependency
```
python 2.7, theano
```
# Data

In this code files like <strong>/AKASHDP_DATA3/ankur/train/A1-NAO-25-FEB-2016-100907-R2-1_b1.rad_boost </strong>are binary files which contain the noisy and denoised images during training. The function <strong>read_filename</strong> reads these binary files and conversts them into stacked numpy arrays suitable for training.

# Training

For training your own model, set the desired configuration of the stacked autoencoders and ensure the availability of suitable data in the form of binary files. Run:

```
python denoise_function.py
```

# Results

## Graphs
### Variation of training process with different patch size
![](https://raw.githubusercontent.com/utkarshojha/image-denoising/master/graphs/patches_plot.png)
### Variation of training process with different number of hidden layers
![](https://raw.githubusercontent.com/utkarshojha/image-denoising/master/graphs/variation_with_no_of_hidden_layers1.png)
### Variation of training process with different sizes of hidden layers
![](https://raw.githubusercontent.com/utkarshojha/image-denoising/master/graphs/variation_with_size_of_hidden_layers.png)

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