Single image haze removal

A Python2 implementation of single image haze removal

The propose of this repository is to implement the image haze removal base on the Zhiming Tan Et al. paper.

Usage

The code can be executed via terminal

python dehaze.py then input the PATH_TO_IMAGE imagename.png

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Sample input

Sample output

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How does it work.

This dehaze algorithm contains three steps,

1. Determine intensity of atmospheric light
2. Estimate transmission map
3. Clarify image

First, the intensity of atmospheric light A is estimated form hazed image I(x). Then, the transmission map t(x) is estimated using A and I(x). Finally, the image is clarified with the image defogging model.

Step#1 Estimate intensity of atmospheric light:

Find the top 0.1% brightest pixels in the dark channel then choose one with highest intensity as the representing of atmospheric light.

Step#2 Estimate transmission map:

First, find a dark channel based on a local area(coarsemap) Then, the transmission map t(x) is thereby obtained:

t(x) = 1 – defoggingParam * darkPixelFromCoarseMap / AtmosphericLightIntensity

The defoggingParam is a value between 0 to 1. The higher value the lesser amount of fog would be kept for the distant objects.

Step#3 Clarify image:

Finally, the image is clarified by: J(x)=(I(x)- A)/max(t(x), t0)+A

Where J(x) is output, I(x) is input, t(x) is transmission map, A is atmospheric light and t0 is set to a constant value to avoid dividing by zero.