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

The code can be executed viaterminal

python dehaze.pythen input the PATH_TO_IMAGEimagename.png

This dehaze algorithm contains three steps,

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

First, the intensity of atmospheric lightA is estimated form hazed imageI(x). Then, the transmission mapt(x) is estimated usingA andI(x). Finally, the image is clarified with the image defogging model.

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

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

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

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

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

WhereJ(x) is output,I(x) is input,t(x) is transmission map,A is atmospheric light andt0 is set to a constant value to avoid dividing by zero.