efficient image/video dehazing through haze density analysis
TRANSCRIPT
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
Overview of image dehazing
Motivation
Background review
Single image haze removal using dark channel Prior
Proposed method
Hazing imaging model
Atmospheric light estimation
Transmission map estimation and refinement
Radiance recovery
Experimental results
Conclusions and discussions
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
Before dehazing After dehazing
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
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
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
Single Image Haze Removal using
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
Single Image Haze Removal using
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
Single Image Haze Removal using
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
Single Image Haze Removal using
Dark Channel Prior
14
Transmission map before refine
Haze image
Dehazed image
Atmospheric light
estimation
Transmission map
estimation
Radiance recovering
Refine
transmission map
Single Image Haze Removal using
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
Single Image Haze Removal using
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
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
The Proposed Dehazing Method:
Observation
18
Different haze density
The Proposed Dehazing Method:
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
The Proposed Dehazing Method:
Atmospheric Light Estimation
20
Haze image
Dehazed image
Pixel-based
dark channel prior
Pixel-based
bright channel prior
HSV distance
Atmospheric light
mapping function
Atmospheric light estimation
Transmission map
estimation
Atmospheric light
estimation
Radiance recovery
Refine
transmission map
Haze image
Dehazed image
The Proposed Dehazing Method:
Atmospheric Light Estimation
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
The Proposed Dehazing Method:
Atmospheric Light Estimation
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
The Proposed Dehazing Method:
Atmospheric Light Estimation
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
The Proposed Dehazing Method:
Atmospheric Light Estimation
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
The Proposed Dehazing Method:
Atmospheric Light Estimation
25
Transmission map
estimation
Ac
s(x)
Atmospheric light
estimation
c
lowestA
c
highestA
Radiance recovery
Refine
transmission map
Haze image
Dehazed image
The Proposed Dehazing Method:
Atmospheric Light Estimation
26
Transmission map
estimation
Ac c
highestA
c
lowestA
Atmospheric light
estimation
Radiance recovery
s(x)
Refine
transmission map
Haze image
Dehazed image
The Proposed Dehazing Method:
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
The Proposed Dehazing Method:
Transmission Map Estimation
28
Transmission map
estimation
Transmission map before refinement
Atmospheric light
estimation
Radiance recovery
Refine
transmission map
Haze image
Dehazed image
The Proposed Dehazing Method:
Refine Transmission Map
29
Transmission map
estimation
Transmission map after Bilateral filter
Atmospheric light
estimation
Radiance recovery
Refine
transmission map
Haze image
Dehazed image
The Proposed Dehazing Method:
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
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
Experimental Results
32
Original image
Proposed
Dehazing method
He’s dehazing results [1]
2011
Fattal’s dehazing results [2]
2008
33
34
Video Dehazing
35
Before dehazing After dehazing
Outline
36
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
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