zhangfan98/Hazing-and-Dehazing-Project

CSE4019: Image Processing

Hazing and Dehazing image processing project

CSE4019: Image Processing at VIT, Vellore.

Dehazing - Color Attenuation

Implementation of Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior.

Research Paper

• Q. Zhu, J. Mai, and L. Shao ,"A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior", in IEEE Transactions On Image Processing, Vol. 24, NO. 11, NOVEMBER 2015, pp. 3522-3533

Dependencies

1. Python 3.6+
2. Python Packages
   • numpy
   • cv2
   • matplotlib

Instructions

folder structure:
    dehazed.jpg # dehazed output
    vit.jpg # sample input image
    room.jpeg
    dehaze.py # main

To dehaze:
    run python3 dehaze.py vit.jpg the output 'dehazed.jpg' will be saved in the same folder

Contributions

• Original author - TummanapallyAnuraag
• Made code reproducible - jacobjohn2016

Dehazing - Dark Channel Prior

This program implement single image dehazing using dark channel prior.

Research Papers

• He, Kaiming, Jian Sun, and Xiaoou Tang. "Single image haze removal using dark channel prior." IEEE transactions on pattern analysis and machine intelligence 33.12 (2011): 2341-2353.
• He, Kaiming, Jian Sun, and Xiaoou Tang. "X.: Guided image filtering." In: ECCV. 2010.

Dependencies

1. Python 3.7
2. Python Packages
   • numpy
   • cv2
   • matplotlib

Instructions

folder structure:
    image # image folder
        14.png
        15.png # main image
        16.png
        J.png
    dehaze.py # file to execute

To dehaze:
    1. save your image file as 15.png in the ./image folder
    2. run python3 dehaze.py the output 'J.png' will be saved in the same folder

Examples

Blog

• How dehazing works: a simple explanation

Contributions

• Original author - He-Zhang
• Made code reproducible - jacobjohn2016

Hazing

Research Paper

• Zhang, Ning, Lin Zhang, and Zaixi Cheng. "Towards Simulating Foggy and Hazy Images and Evaluating Their Authenticity." International Conference on Neural Information Processing. Springer, Cham, 2017.

License:

This code is made publicly for research use only. 
It may be modified and redistributed under the terms of the GNU General Public License.
Please cite the paper and source code if you are using it in your work.

Dependencies

1. Python 3.6+
2. Python Packages
   • numpy
   • cv2
   • math
   • matplotlib
   • tool_kit
   • scipy.io
   • noise
   • PIL

Instructions:

folder structure:
      FoHIS/const.py  # define const
            fog.py  # main
            parameter.py # all parameters used in simulating fog/haze are defined here.
            tool_kit.py # some useful functions
            
      AuthESI/compute_aggd.py
              compute_authenticity.py  # main
              guided_filter.py  # some functions
              prisparam_16_hazeandfog.mat  # pre-trained model
              
      img/img.jpg  # RGB image
          imgd.jpg  # depth image
          result.jpg  # simulation
          
1. To simulate fog/haze effects:
    run python FoHIS/fog.py, the output 'result.jpg' will be saved in ../img/
      
2. To evaluate the authenticity:
    run python compute_authenticity.py to evaluate 'result.jpg' in ../img/

Dataset:

Source Image	Maximum Depth	Effect	Homogeneous	Particular Elevation
(a)	150 m	Haze	Yes	No
(b)	400 m	Haze	Yes	No
(c)	800 m	Haze	Yes	No
(d)	30 m	Fog	Yes	No
(e)	150 m	Fog	No	Yes
(f)	30 m	Fog+Haze	No	No
(g)	600 m	Haze	Yes	No
(h)	400 m	Haze	Yes	No
(i)	200 m	Haze	Yes	No
(j)	100 m	Haze	Yes	No
(k)	100 m	Haze	Yes	No
(l)	800 m	Fog+Haze	No	Yes
(m)	300 m	Haze	Yes	No
(n)	60 m	Haze	Yes	No
(o)	300 m	Haze	Yes	No
(p)	1000 m	Haze	Yes	No
(q)	400 m	Haze	Yes	No
(r)	300 m	Haze	Yes	No

Contributions

• Original author - noahzn (Noah)
• Made code reproducible - jacobjohn2016