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1. Raskar, Camera Culture, MIT Media Lab
Computational Ph t
C t ti l Photography:
h
Epsilon to Coded Imaging
p g g
Camera Culture
Ramesh Raskar
Camera Culture
C C lt
Associate Professor, MIT Media Lab http://raskar.info
2.
3. Tools
for
Visual
Computing
Shado Refracti Reflecti
w ve ve
Fernald, Science [Sept 2006]
4. How can we create an entirely
new class of imaging platforms
that have an understanding of the world that far exceeds
human ability
and produce meaningful abstractions that are well
within h
ithi human comprehensibility ?
h ibilit
Ramesh Raskar http://raskar.info
5. Raskar 2006
Computational Illumination Augmented Reality
Mitsubishi Electric Research Laboratories Spatial
Planar Non planar
Non-planar Curved Objects Pocket Proj
Pocket-Proj
1998 1997 2002 2002
Single
Projector
Use
r:T j
?
Projector
1998 1998 2002 1999 2003
Multiple
Projectors
Computational Camera and Photography
8. Short
Sh t Traditional
T diti l MURA
Exposure Shutter
Captured
Single
Photo
Ph t
Deblurred
Result
Banding Artifacts and
Dark
some spatial frequencies
and noisy
d i
are lost
9. Blurring == Convolution
Fourier
Transform
Sharp
Sh Blurred
Bl d
Photo PSF == Sinc Function Photo
ω
Traditional Camera: Shutter is OPEN: Box Filter
10. Fourier
Transform
Sharp
Sh Blurred
Bl d
Photo PSF == Broadband Function Photo
Preserves High Spatial
Frequencies
Flutter Shutter: Shutter is OPEN and CLOSED
11. Flutter Shutter Camera
Raskar, Agrawal, Tumblin [Siggraph2006]
LCD opacity switched
in coded sequence
12. Traditio Coded
nal Exposu
re
Deblurred Deblurred
Image
I Image
I
Image of
Static
Object
26. Mask Sensor
Mask
?
Sensor
Mask
Full Resolution Digital 4D Light Field from
Refocusing: 2D Photo:
Coded Aperture Camera Heterodyne Light Field
d h ld
Camera
40. Glare = low frequency noise in 2D
•But is high frequency noise in 4D
•Remove via simple outlier rejection
Sensor
i
j
u x
41. Rays = Waves for Propagation and Interface
Fresnel propagation Chirp (Lens) Fourier transform Fractional Fourier transform
x1 x2 x3 x4
x0
b
¡ a x0
u1 u2 u3
b u4
-ax
b 0
x0 x1 x0 x2 x3 -bx
a 0 x4
x0
- a
-x
a
0
I
x4
42. Imaging via volume hologram (Depth-specific Imaging)
KVH ( x =0, u =θ /λ; x , u )
4 4 s 3 3
-20
u4
1
u3
-15
0.8
-10
0.6
-5
L
u3 [mm-1]
0 0.4 x3 x4
5
0.2
10
0
15
20 -0.2
-0.4 -0.2 0 0.2 0.4
x3 [mm]
ZZ ½ µ ¶¾
0 0 µs
dx 0 dx 0 e¡ i 2¼( u 4 x 4 ¡ u 3 x 3 )
K V H (x 4 ; u4 ; x 3 ; u3 ) =3 4
0 0
exp ¡ i 2¼ zf (u3 + u4 ) ¡ u3 + u4 ¡
¸
¸
½ µ 0 0
¶µ 0
¶¾ ½ µ 0 0
¶µ ¶¾
u3 + u4 u4 µs u3 + u4 u0
4 µs
£ sinc L ¸ ¡ u3 + u4 + u4 + ¡ sinc L ¸ ¡ u3 + u4 ¡ u4 ¡ ¡
2 2 ¸ 2 2 ¸
½ ¾ ½ ¾
¼ zf 2 L
Derivation: h(x 2 ; x 1 ) = exp ¡ i 2
(x 1 + x 2 ¡ f µs) sinc (x 1 + x 2 ) (x 2 ¡ f µs)
¸ f ¸f2
K V H I (x 2 ; u2 ; x 1 ; u1 ) Parameters:
¸=05¹
0.5 ¹m
µs= 30°
K V H (x 4 ; u4 ; x 3 ; u3 ) L = 1 mm
zf = 50 mm
44. How can we create an entirely
new class of imaging platforms
that have an understanding of the world that far exceeds
human ability
and produce meaningful abstractions that are well
within h
ithi human comprehensibility ?
h ibilit
Ramesh Raskar http://raskar.info