Phil Morley The Problem Fog Haze or Smog Want a clear image Weather could be common in areas The Method Outlined in paper Single Haze Removal Using Dark Channel Prior by Kaimin He ID: 304033
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Slide1
Haze Removal
Phil MorleySlide2Slide3Slide4
The Problem
Fog, Haze, or Smog
Want a clear image
Weather could be common in areasSlide5
The Method
Outlined in paper:
Single Haze Removal Using Dark Channel Prior
by Kaimin He,
Jian Sun, and Xiaoou
Tang Slide6
What is haze?
I
(x)
= J
(x)t(x) + A(1 − t
(x))
I(x): Image
J(x): Scene RadianceA: Atmospheric Lightt(x): TransmittanceSlide7
Dark Channel Prior
Objects of interest have low values in at least one color channel
Green leaf
Car Shadow
Dark buildingHaze has a high pixel intensitySlide8Slide9
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))Slide10
Estimating Transmission
Shuffling the Haze Equation and taking min’s gives you:
Which is simply:Slide11
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.Slide12
Soft Matting
Minimize Cost Function:
Has Closed Form Solution:
U
3
= 3x3 Identity
λ
= 0.0001Slide13
Things to improve
Performance
Processing Time
Memory Allocation
SettingsSlide14
Things To Expand
Depth Map
From Transmittance
3D Model
Image EnhancementHistogram EqualizationSlide15
Current ResultsSlide16Slide17Slide18Slide19Slide20