Efficient Image/Video Dehazing through Haze Density Analysis ...

Efficient Image/Video Dehazing through

Haze Density Analysis Based on Pixel-based

Dark Channel Prior

1

Outline

2

Introduction

Overview of image dehazing

Motivation

Background review

Single image haze removal using dark channel prior [1]

Proposed method

Hazing imaging model

Atmospheric light estimation

Transmission map estimation and refinement

Radiance recovery

Experimental results

Conclusions and discussions

Outline

3

Introduction


Motivation

Background review

Single image haze removal using dark channel Prior

Proposed method




Radiance recovery



Overview of Image Dehazing

4

What is image dehazing?

Image dehazing is a technique of image processing that can

increase the image quality from an input with haze

Haze

images

Dehazed

images

Motivation

5

Why is image dehazing?

Dehazing could increase the visibility for driving assistance

Provide the clear input image for applications

Pedestrian Detector Comparison

6

Pedestrian Detections [threshold = 0.00]

0.25

0.17

0.11

Pedestrian Detections [threshold = 0.00]

0.99

0.74

0.69 0.980.70

0.61

0.01

Before dehazing After dehazing

S. Maji, A. C. Berg, and J. Malik, “Classification using intersection kernel support vector machines is efficient,” in

Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Anchorage, Alaska, USA, June 2008, pp. 1-8.

Blob Detector

7


M. Jahangiri and M. Petrou, “An attention model for extracting components that merit identification,” in Proc.

IEEE Int. Conf. Image Processing, Cairo, Egypt, Nov. 2009, pp. 965-968.

Outline

8

Introduction


Motivation

Background review

Single image haze removal using dark channel Prior [1]

Proposed method




Radiance recovery



Haze Imaging Model

9

Atmosphere light Camera

Clear image

Transmission Scattering

Haze image

Haze

A widely used haze imaging model is present as:

J(x)：haze free image

A：atmospheric light

t(x)：transmission

))t(-A(1))t(J()I( xxxx

Single Image Haze Removal using

Dark Channel Prior

10

Haze image

Dehazed image

Atmospheric light

estimation

Transmission map

estimation

Radiance recovering

Define patch-based dark channel prior：

dark channel

prior

)))((min(min)()(},,{

yxxybgrc

cdark JJ

)(x : a local patch center at x

Jc : a color channel of J

Refine

transmission map


Dark Channel Prior

11

Find the 0.1% brightest point in dark channel prior

Haze image

Dehazed image

Atmospheric light

estimation

Transmission map

estimation

Radiance recovering

Refine

transmission map


Dark Channel Prior

12

Mapping to the original image, and choose the pixel value

as atmospheric light color Ac

Haze image

Dehazed image

Atmospheric light

estimation

Transmission map

estimation

Radiance recovering

Refine

transmission map


Dark Channel Prior

13

The haze imaging model reduced by dark channel

prior is described as:

According to [1], the haze image model modified by:

The estimated transmission function is:

ccc A1J)t(I ))(())((min(min))((min(min)(},,{)(},,{

xtyxyxybgrcxybgrc

0))(J(min(min c

)(},,{

y

xybgrc

))A

)(I(min(min1)t(

c

c

)(ω},,{

ywx

xybgrc

Where w fixed to 0.95 in paper [1]

Haze image

Dehazed image

Atmospheric light

estimation

Transmission map

estimation

Radiance recovering

Refine

transmission map


Dark Channel Prior

14

Transmission map before refine

Haze image

Dehazed image

Atmospheric light

estimation

Transmission map

estimation

Radiance recovering

Refine

transmission map


Dark Channel Prior

15

Transmission map after refinement by Soft matting

Haze image

Dehazed image

Atmospheric light

estimation

Transmission map

estimation

Radiance recovering

Refine

transmission map


Dark Channel Prior

16

c

0

c

Att

AIJ

)),(max(

)()(

x

xx

J(x) : the dehazed pixels

I(x) : the input pixels

t(x) : the transmission value

t0 : the lower bound of transmission, paper [1] set as 0.1

Ac : the air light color

Finally, the haze free image is recovered by:

Haze image

Dehazed image

Atmospheric light

estimation

Transmission map

estimation

Radiance recovering

Refine

transmission map

Outline

17

Introduction


Motivation

Background review


Proposed method




Radiance recovery



The Proposed Dehazing Method:

Observation

18

Different haze density


Haze Imaging Model

19

Proposed pixel-based haze image model is present as:

J(x) : haze free image

A(x) : pixel-based atmospheric light

t(x) : transmission

))t(-A(x)(1))t(J()I( xxxx

Atmosphere light Camera

Clear image

Transmission Scattering

Haze image

Haze


Atmospheric Light Estimation

20

Haze image

Dehazed image

Pixel-based

dark channel prior

Pixel-based

bright channel prior

HSV distance

Atmospheric light

mapping function


Transmission map

estimation

Atmospheric light

estimation

Radiance recovery

Refine

transmission map

Haze image

Dehazed image



21

Define pixel-based dark channel prior：

))(J(min)(J c

},,{

dark_Pix xxbgrc

index pixelsx

pixel-based dark

channel

prior

Transmission map

estimation

Jc : color channel of J

Atmospheric light

estimation

Radiance recovery

Refine

transmission map

Haze image

Dehazed image

pixel-based

bright channel

prior



22

Define pixel-based bright channel prior：

))(J(max)(J c

},,{

bright_Pix xxbgrc

Transmission map

estimation

Jc : color channel of J

Atmospheric light

estimation

index pixelsx

Radiance recovery

Refine

transmission map

Haze image

Dehazed image



23

||W)(I|| )d( HSV xx

W: the brightest pixel in HSV

Define HSV distance：

Transmission map

estimation

Atmospheric light

estimation

Radiance recovery

Refine

transmission map

Haze image

Dehazed image



24

Transmission map

estimation )s(|

))(d/11(min))(d/11(max

AA| )(A

HSVHSV II

c

lowest

c

highestc xxy

x

yy

c

highestAis the brightest value of Jdark_Pix mapping to original

image

is the darkest value of Jbright_Pix mapping to original

image

c

lowestA

Atmospheric light estimating function present as:

Atmospheric light

estimation

Radiance recovery

))(max(d

)(d1 )s(

x

xx

Refine

transmission map

Haze image

Dehazed image



25

Transmission map

estimation

Ac

s(x)

Atmospheric light

estimation

c

lowestA

c

highestA

Radiance recovery

Refine

transmission map

Haze image

Dehazed image



26

Transmission map

estimation

Ac c

highestA

c

lowestA

Atmospheric light

estimation

Radiance recovery

s(x)

Refine

transmission map

Haze image

Dehazed image


Transmission Map Estimation

27

Transmission map

estimation

)())(()(min)(min},,{},,{

xxxxxbgrcbgrc

ccc At1J)t(I

0Jc

)(min},,{

xbgrc

)A

I(1t

c

c

)(

)(min)(

},,{ x

xwx

bgrc

The haze image model reduced by pixel-based dark

channel prior is described as:

According to [1], the haze image model modified by:

The estimated transmission function is:

Where w fixed to 0.95 in paper [1]

Atmospheric light

estimation

Radiance recovery

Refine

transmission map

Haze image

Dehazed image


Transmission Map Estimation

28

Transmission map

estimation

Transmission map before refinement

Atmospheric light

estimation

Radiance recovery

Refine

transmission map

Haze image

Dehazed image


Refine Transmission Map

29

Transmission map

estimation

Transmission map after Bilateral filter

Atmospheric light

estimation

Radiance recovery

Refine

transmission map

Haze image

Dehazed image


Radiance Recovery

30

Transmission map

estimation

Radiance recovery

Refine

transmission map

)()),(max(

)()()( x

x

xxx c

0

c

Att

AIJ

J(x) : the dehazed pixels

I(x) : the input pixels

t(x) : the transmission value

t0 : the lower bound of transmission, we fix it as 0.1

Ac(x) : the air light color

Finally, the haze free image J(x) is recovered by: Atmospheric light

estimation

Outline

31

Introduction


Motivation

Background review


Proposed method




Radiance recovery



Experimental Results

32

Original image

Proposed

Dehazing method

He’s dehazing results [1]

2011

Fattal’s dehazing results [2]

2008

Video Dehazing

35


Outline

36

Introduction


Motivation

Background review


Proposed method




Radiance recovery



Conclusions and Discussions

37

The pixel-based dark/bright channel prior and fog density

estimate method have been proposed in this thesis

Our proposed method can accurately estimate the

atmospheric light via haze density analysis

The adaptive atmospheric light provide the high accuracy of

transmission map

Efficient Image/Video Dehazing through Haze Density Analysis ...

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