python implementation of the paper: "Efficient Image Dehazing with Boundary Constraint and Contextual Regularization"

pip install image_dehazer

Usage:

$ import image_dehazer										# Load the library

$ HazeImg = cv2.imread('image_path')						# read input image -- (**must be a color image**)
$ HazeCorrectedImg = image_dehazer.remove_haze(HazeImg)		# Remove Haze

$ cv2.imshow('input image', HazeImg);						# display the original hazy image
$ cv2.imshow('enhanced_image', HazeCorrectedImg);			# display the result
$ cv2.waitKey(0)											# hold the display window

1. Go to the src folder
2. run the file "example.py"
3. sample images are stored in the "Images/" folder
4. Output images will be stored in the "outputImages/" folder

1.numpy==1.19.0

2.opencv-python

3.scipy

This code is an implementation of the paper "Efficient Image Dehazing with Boundary Constraint and Contextual Regularization" The algorithm can be divided into 4 parts:

• Airlight estimation
• Calculating boundary constraints
• Estimate and refine Transmission
• Perform Dehazing using the estimated Airlight and Transmission

• This project is licensed under the BSD 2 License - see the LICENSE.md file for details

• The author would like to thank Gaofeng MENG, Ying WANG, Jiangyong DUAN, Shiming XIANG, Chunhong PAN for their paper "Efficient Image Dehazing with Boundary Constraint and Contextual Regularization"

• The author would like to thank Alexandre Boucaud. The function psf2otf was obtained from his repository. (https://github.com/aboucaud/pypher/blob/master/pypher/pypher.py)

• The Author would like to thank Dr. Suresh Merugu for his matlab implementation of the codes. This repository is the python implementation of the matlab codes.

Merugu, Suresh. (2014). Re: How to detect fog in an image and then enhance the image to remove fog?. Retrieved from: https://www.researchgate.net/post/How_to_detect_fog_in_an_image_and_then_enhance_the_image_to_remove_fog/53ae3f10d2fd64c3648b45a9/citation/download.