Deep-Speckle-Correlation

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Deep-Speckle-Correlation

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python implementation of Deep speckle correlation: a deep learning approach towards scalale imaging through scattering media

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최종 버전 다운로드 (.zip)

- img1.png
- img2.png
- img3.png
- letter_E.npy
- letter_S.npy
- number_8.npy
- number_9.npy
.gitattributes
demo.py
LICENSE
model.py
README.md
traning code.py

// repository documentation

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# Deep-Speckle-Correlation
Python implementation of paper: **Deep speckle correlation: a deep learning approach towards scalable imaging through scattering media**. We provide model, pre-trained weights(download link available below), test data and a quick demo.

### Citation
If you find this project useful in your research, please consider citing our paper:

[**Yunzhe Li, Yujia Xue, and Lei Tian, "Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media," Optica 5, 1181-1190 (2018)**](https://www.osapublishing.org/optica/abstract.cfm?uri=optica-5-10-1181)

### Abstract
Imaging through scattering is an important, yet challenging problem. Tremendous progress has been made by exploiting the deterministic input-output transmission matrix for a fixed medium. However, this one-for-one approach is highly susceptible to speckle decorrelations -- small perturbations to the scattering medium lead to model errors and severe degradation of the imaging performance. Our goal here is to develop a new framework that is highly scalable to both medium perturbations and measurement requirement.  To do so, we propose a statistical one-for-all deep learning technique that encapsulates a wide range of statistical variations for the model to be resilient to speckle decorrelations. Specifically, we develop a convolutional neural network (CNN) that is able to learn the statistical information contained in the speckle intensity patterns captured on a set of diffusers having the same macroscopic parameter. We then show for the first time, to the best of our knowledge, that the trained CNN is able to generalize and make high-quality object prediction through an entirely different set of  diffusers of the same class. Our work paves the way to a highly scalable deep learning approach for imaging through scattering media.

### Requirements
python 3.6

keras 2.1.2

tensorflow 1.4.0

numpy 1.14.3

h5py 2.7.1

matplotlib 2.1.2

### CNN architecture

 <img src="/images/img2.png">

### Download pre-trained weights
You can download pre-trained weights from [here](https://zenodo.org/records/14939667?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6IjYxN2Q4ZDM0LTc3MWQtNDdiOS04MDY2LWQzYjM5MmFkZGE1YSIsImRhdGEiOnt9LCJyYW5kb20iOiI4N2I4MzY5YjNkZjRmMjMzYTAyMTFiMDI5NjQwYzk0NiJ9._LjMaI7t13wCyQA6MF4cBMacQ9SI8GrmuwaTBiIOKWfRrldPYZRJxjHKr4kvciulcubskLhg8xF_U55eEqGCnQ)

### Download dataset
You can download dataset from [here](https://zenodo.org/records/15361263?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6IjA4NWZjOWNkLTAwNzctNGIyNi04ODNkLTIzOTIxYzA2NTg1ZCIsImRhdGEiOnt9LCJyYW5kb20iOiIwMTVlNTA0YjE2N2RjNTQ3NjlmOTQ4ZWM1MDE3MmY4NyJ9.7FuO7_HZdT-pXfJY5NHey6tZ_H4YwC1QEYfROznirjCO_OZNawN-CpaB6Brb6Qrona-rabd3NeOcQWlNAcOPwg)
#### Data for training:
Letter speckle: each diffuser we use 300 images for training [image and corresponding ground truth index range:0-299)]  
Digit speckle: each diffuser we use 300 images for training [image and corresponding ground truth index range:0-299)]

#### Data for testing:
Seen digits and letters: each unseen diffuser 400 images.  
Including:  
200 letters [image index 0-199)] corresponding letter ground truth index 0-199  
200 digits [image index 200-399] corresponding digits ground truth index 0-199  
Unseen digits and letters  
Unseen digits:  100 images are used for testing [image and corresponding ground truth index range 300-399]  
Unseen digits: 100 images are used for testing [image and corresponding ground truth index range 300-399]  
Quickdraw : different object types

### How to use
After download the pre-trained weights file, put it under the root directory and run [demo.py](demo.py).

### Results

 <img src="/images/img3.png">

## License
This project is licensed under the terms of the MIT license. see the [LICENSE](LICENSE) file for details

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