Haze Removal

HAZE REMOVALPhil Morley

The Problem Fog, Haze, or Smog Want a clear image Weather could be common in areas

The MethodOutlined in paper: Single Haze Removal Using Dark Channel Prior by Kaimin He, Jian Sun, and Xiaoou Tang

What is haze?

I(x) = J(x)t(x) + A(1 − t(x))

I(x): ImageJ(x): Scene RadianceA: Atmospheric Lightt(x): Transmittance

Dark Channel Prior Objects of interest have low values in at

least one color channelGreen leafCar ShadowDark building

Haze has a high pixel intensity

Compute Atmospheric Light

High values in Dark Channel Take top 0.1% Pull Values from original image Average

I(x) = J(x)t(x) + A(1 − t(x))

Estimating TransmissionShuffling the Haze Equation and taking min’s gives you:

)))((min(min1)(~)( A

yIxtc

c

xyc

Which is simply:

][ )(1)(~AyIxtdark

Refine Transmission with Soft Matting Estimated Transmission is blocky

Want to take into account fine detail Haze Equation is alpha matting Therefore can use Soft Matting as

shown by Levin et al.

Soft Matting

)~()~()( ttttttt TT LE

Minimize Cost Function:

Has Closed Form Solution:

tt ~)( UL

wjik

kjk

kk

T

kij

k

Uwiw

jiL),|(

3

1

)))()()(1(1),(

II

U3 = 3x3 Identityλ = 0.0001

Things to improve Performance

Processing TimeMemory Allocation

Settings

Things To Expand Depth Map

From Transmittance3D Model

Image EnhancementHistogram Equalization

Current Results