https://github.com/shamil-t/under_water-image-enhancement

under water image enhancement and restoration with reef classifcation
https://github.com/shamil-t/under_water-image-enhancement

clahe dataset dcp django heroku-app image-restoration mip noises python3 quality rayleigh-distribution underwater-images water-image

Last synced: 19 days ago
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under water image enhancement and restoration with reef classifcation

• Host: GitHub
• URL: https://github.com/shamil-t/under_water-image-enhancement
• Owner: shamil-t
• Created: 2021-02-01T14:45:21.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2022-04-30T16:39:23.000Z (over 2 years ago)
• Last Synced: 2024-10-08T01:23:23.025Z (30 days ago)
• Topics: clahe, dataset, dcp, django, heroku-app, image-restoration, mip, noises, python3, quality, rayleigh-distribution, underwater-images, water-image
• Language: Python
• Homepage: https://under-water-image-enhancement.herokuapp.com/
• Size: 4.69 MB
• Stars: 5
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# 🆄🅽🅳🅴🆁 🆆🅰🆃🅴🆁 🅸🅼🅰🅶🅴 🅴🅽🅰🅽🅲🅴🅼🅴🅽🆃 🅰🅽🅳🅳 🆁🅴🆂🆃🅾🆁🅰🆃🅸🅾🅽 : 🆁🅴🅴🅵 🅲🅻🅰🆂🆂🅸🅵🅸🅲🅰🆃🅸🅾🅽

The main objective of the Project is to reduce the noises in the Underwater Images.

We propose some methods for efficient removal of Noises using Image Processing

Techniques.

  The Underwater images have low quality which makes it a difficult process to analyze

the images. Here we propose Image Enhancement and Image Restoration process for

increasing the quality of Underwater Images. Clahe, Reyleigh distribution, DCP and

MIP,RGHS,ULAP methods are used in this project.

### IMAGE ENHANCEMENT

- CLAHE - CONTRAST LIMITED ADAPTIVE HISTOGRAM EQUALIZATION

- RAYLEIGH DISTRIBUTION

- RGHS - Relative Global Histogram Stretching

### IMAGE RESTORATION

- DCP - DARK CHANNEL PRIOR

- MIP - MAXIMUM INTENSITY PROJECTION

- ULAP - Underwater Light Attenuation Prior

## 🅿🆁🅴 🆁🅴🆀🆄🅴🆂🆃🅸🅴🆂

### Environment Setup

- python 3.8.6 64bit

- install dependencies `$ pip install -r requirements.txt`

- download models from [here](https://drive.google.com/drive/folders/1euqJMjlMuDJpLOWRTrMhzEG_RH62cdiw?usp=sharing "link title"), place it in models folder`/UWIE/CLASSIFY/models/`

### Dataset

- Pocillopora

- Acropora

- Turf

[Download DataSet from here](http://vision.ucsd.edu/~beijbom/moorea_labeled_corals/patches/)

## 🅷🅾🆆🆆 🆃🅾 🆁🆄🅽

`$ py manage.py runserver`

Demo project hosted on heroku [link](https://under-water-image-enhancement.herokuapp.com/)