High-Quality Computational Imaging Through Simple Lens

High-Quality Computational Imaging
Through Simple Lenses
F. Heide1
, M. Rouf1
, M. Hullin1
, B. Labitzke2
, W. Heidrich1
, A. Kolb2
1
University of British Columbia, 2
University of Siegen
(ACM Transactions on Graphics, 2013)
Presented by Monica Drăgan

Advanced Methods in Computer Graphics, SS2014Freitag, 4. April 2014
Correct for :
●
Geometric distortion
●
Spherical aberation
●
Chromatic aberation
●
Coma

Correct for :
●
●
Spherical aberation
●
Chromatic aberation
●
Coma
expensive, large, heavy

Correct for :
●
●
Spherical aberation
●
Chromatic aberation
●
Coma
expensive, large, heavy
Alternative
approach
to high-quality
photography

Correct for :
●
●
Spherical aberation
●
Chromatic aberation
●
Coma
COM
PU
TATION
ALLY
Simple lenses:
●
Plano-convex
●
Biconvex
●
Achromatic doublets

Blurred captured image

Corrected image

Outline
●
What are the challenges?
●
Previous work
●
Paper contribution
●
Current approach
●
Performance
●
Applications
●
Future work

Point spread function (PSF)
f/2.0 f/4.5

Point spread function (PSF)
●
Spatially large (50x50px)
●
Spatial variation
●
Wavelength dependent

Image convolution
Blur
kernel
=
Observed
blurred image
Underlying
sharp image
*

Deconvolution
Blur
kernel
=
Observed
blurred image
Underlying
sharp image
*
-1

Deconvolution
*
-1
=
*
-1
=
*
-1
=

Previous work
Levin et al. '07
Idependent
deconvolution
on each
color channel
Schuler et al. '11
Deconvolution in
YUV space
Cossairt & Nayar '10
Use chromatic
aberations to
increase DOF

Outline
●
●
Previous work
●
Paper contribution
●
Current approach
●
Performance
●
Limitations
●
Future work

Paper contribution
●
Cross-channel instead of channel-independent deconvolution

Paper contribution
●
Observations:
●
hue changes are sparse and occure near the edges
●
edges appear in the same place in all channels
Blurred image Sharp image

Paper contribution
●
Severe
ringing
Residual
blur
Levins '07 Current approachBlurred image

Paper contribution
●
●
Efficient convex optimization solver [Chambolle & Pock '11]

Paper contribution
●
●
●
Robust approach for per-channel PSF estimation

Paper contribution
●
●
●
Robust approach for per-channel PSF estimation
White noise calibration pattern

Current approach
*
-1
=
*
-1
=
*
-1
=
estimate
PSF
(lens specific)

Current approach
*
-1
=
*
-1
=
*
-1
=
formulate
the optimization
problem
estimate
PSF
(lens specific)

-*
Optimization problem
Least squares
data fitting

Sparse
image gradient

Cross-channel prior

Least squares
data fitting
Cross-channel prior
Sparse
image gradient
Weighted contributions

Least squares
data fitting
Cross-channel prior
Sparse
image gradient
PROBLEM IS CONVEX

Least squares
data fitting
Cross-channel prior
Sparse
image gradient
PROBLEM IS CONVEX
Efficiently solvable by standard
forward – backward splitting methods
[Chambolle & Pock '11]

Original Resulted

Regularization for
low intensity areas
needed
Original
Resulted

Original Resulted
Unscaled gradients

Improved result
Original Initial result

Least squares
data fitting
Cross-channel prior
Regularization for
low intensity pixels
Sparse
image gradient

PSF estimation
●
PSF calibration – with white noise pattern
●
for a certain aperture
I J
f/2.0 f/4.5

PSF estimation
●
PSF calibration – with white noise pattern
●
●
for each tile
I J
f/2.0 f/4.5

PSF estimation
●
PSF calibration – with wite noise pattern
●
●
for each tile
I JB
* ? =

PSF estimation
●
●
●
for each tile
●
Non-blind deconvolution
Least squares
data fitting
I JB

PSF estimation
●
●
●
for each tile
●
Least squares
data fitting
Energy
conservation
Gradient
total variation
I JB

PSF estimation
●
●
●
for each tile
●
forward – backward splitting methods
[Chambolle & Pock '11]I JB

PSF estimation
●
●
●
for each tile
●

PSF estimation
●
Once per lens
●
Accurate (two consecutive shots with different apertures)

Outline
●
●
Previous work
●
Tool's magic
●
Current approach
●
Performance
●
Applications
●
Future work

Performance
●
Outperforms other state-of-art methods
●
Postprocessing image quality comparable to that of a
compact camera (at f/4.5)
●
Improves also images taken with compact cameras

Original
Deblurred
Levin
Deblurred
Schuler
Current
approach

Applications
●
Deconvolution for multispectral cameras
●
Remove residul blur in regular cameras

Future work
●
Use drastically simpler lens design
●
Optimize lenses to generate blur that is easier to remove
●
Calibrate full depth-dependent PSFs
●
Speed up the computation (distributed computing)
●
Running time: ~10-20 s for a 8MP image

Thank you!
& special thanks to
Marios Papas

Regularization for
low intensity pixels
forward – backward splittin methods
[Chambolle & Pock '11]
Least squares
data fitting
Cross-channel prior
1. Optimization problem
Sparse
image gradient

Original
Original

Deblurred

Running time

High-Quality Computational Imaging Through Simple Lens

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High-Quality Computational Imaging Through Simple Lens