Enhancement-of-Underwater-Images-with-Statistical-Model-of-BL-and-Optimization-of-TM

This is python implementation for a underwater image enhancement paper "Enhancement of Underwater Images with Statistical Model of Background Light and Optimization of Transmission Map"

ABSTRACT! Underwater images often have severe quality degradation and distortion due to light absorption and scattering in the water medium. A hazed image formation model is widely used to restore the image quality. It depends on two optical parameters: the background light (BL) and the transmission map (TM). Underwater images can also be enhanced by color and contrast correction from the perspective of image processing. In this paper, we propose an effective underwater image enhancement method for underwater images in composition of underwater image restoration and color correction. Firstly, a manually annotated background lights (MABLs) database is developed. With reference to the relationship between MABLs and the histogram distributions of various underwater images, robust statistical models of BLs estimation are provided. Next, the TM of R channel is roughly estimated based on the new underwater dark channel prior (NUDCP) via the statistic of clear and high resolution (HD) underwater images, then a scene depth map based on the underwater light attenuation prior (ULAP) and an adjusted reversed saturation map (ARSM) are applied to compensate and modify the coarse TM of R channel. Next, TMs of G-B channels are estimated based on the difference of attenuation ratios between R channel and G-B channels. Finally, to improve the color and contrast of the restored image with a dehazed and natural appearance, a variation of white balance is introduced as post-processing. In order to guide the priority of underwater image enhancement, sufficient evaluations are conducted to discuss the impacts of the key parameters including BL and TM, and the importance of the color correction. Comparisons with other state-of-the-art methods demonstrate that our proposed underwater image enhancement method can achieve higher accuracy of estimated BLs, less computation time, more superior performance, and more valuable information retention.

Terms & Conditions

• The dataset is available for non-commercial research purposes only.
• All images of the dataset are obtained from the Internet and some papers which are not property of Digital Ocean Laboratory (DOL), Shanghai Ocean University. The DOL is not responsible for the content nor the meaning of these images.
• You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data.
• You agree not to further copy, publish or distribute any portion of the DOL. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset.
• The SOL reserves the right to terminate your access to the DOL at any time.

How to get the Password

This database is publicly available. It is free for professors and researcher scientists affiliated to a University. Permission to use but not reproduce or distribute our database is granted to all researchers given that the following steps are properly followed: Send an e-mail to Yan Wang ([email protected]) or Wei Song ([email protected]) before downloading the database. You will need a password to access the files of this database. Your Email MUST be set from a valid University account and MUST include the following text:

1. Subject: (DOL) Application to download the DOL Dataset          
2. Name: <your first and last name>
3. Affiliation: <University where you work>
4. Department: <your department>
5. Position: <your job title>
6. Email: <must be the email at the above mentioned institution>

I have read and agree to the terms and conditions specified in the RAF face database webpage. 
This database will only be used for research purposes. 
I will not make any part of this database available to a third party. 
I'll not sell any part of this database or make any profit from its use.

Install

Here is the list of libraries you need to install to execute the code:

• python = 3.6
• cv2
• numpy
• scipy
• matplotlib
• scikit-image
• natsort
• math
• datetime

Content

This repository contains the files:

• Dataset: contains original images and MABLs
• EnhancementofUnderwaterImages: the raw running code

Easy Usage

1. Complete the running environment configuration;
2. Put the inputs images to corresponding folders :

• (create 'InputImages' and 'OutputImages' folders, then put raw images to 'InputImages' folder);

3. Python main.py;
4. Find the enhanced/restored images in "OutputImages" folder.

Citation

If our database or code proves useful for your research, please cite our review papers and some related papers.

@article{Review of Image Enhancement and Image Restoration Methods,
    author    = {Yan Wang, Wei Song, Giancarlo Fortino, Lizhe Qi, Wenqiang Zhang, Antonio Liotta},
    title     = {An Experimental-based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging},
    journal   = {IEEE Access，DOI:10.1109/ACCESS.2019.2932130},
    year      = {2019}
}
@article{Review of Image Enhancement and Image Restoration Methods,
    author    = {Yan Wang, Wei Song, Giancarlo Fortino, Lizhe Qi, Wenqiang Zhang, Antonio Liotta},
    title     = {An Experimental-based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging},
    journal   = {arXiv:1907.03246},
    year      = {2019}
 }   
@article{Underwater Image Enhancement Method,
    author    = {Wei Song, Yan Wang, Dongmei Huang, Antonio Liotta, Cristian Perra},
    title     = {Enhancement of Underwater Images with Statistical Model of Background Light and Optimization of Transmission Map},
    journal   = {IEEE Transactions on Broadcasting},
    year      = {2019}
}
@article{Underwater Image Enhancement Method,
    author    = {Wei Song, Yan Wang, Dongmei Huang, Antonio Liotta, Cristian Perra},
    title     = {Enhancement of Underwater Images with Statistical Model of Background Light and Optimization of Transmission Map},
    journal   = {arXiv:1906.08673},
    year      = {2019}
}

Contact Authors

• Yan Wang, e-mail: [email protected]
• Wei Song, e-mail: [email protected]