Transcript Inferring the kernel: multiscale method Input image Convert to grayscale Remove gamma correction User selects patch from image Loop over scales Upsample estimates Variational Bayes Initialize 3x3 blur kernel Use multi-scale approach to avoid local.

Inferring the kernel: multiscale method
Input image
Convert to
grayscale
Remove gamma
correction
User selects patch
from image
Loop over scales
Upsample
estimates
Variational
Bayes
Initialize 3x3
blur kernel
Use multi-scale approach to avoid local minima:
Image Reconstruction
Input image
Convert to
grayscale
Remove gamma
correction
User selects patch
from image
Loop over scales
Full resolution
blur estimate
Upsample
estimates
Non-blind deconvolution
(Richardson-Lucy)
Variational
Bayes
Initialize 3x3
blur kernel
Deblurred
image
Results on real images
Submitted by people from their own photo collections
Type of camera unknown
Output does contain artifacts
– Increased noise
– Ringing
Compares well to existing methods
Original photograph
Blur kernel
Our output
Original
Matlab’sphotograph
deconvblind
Close-up of garland
Original
Matlab’s
deconvblind
Our output
A submitted
photograph
A small list of the reasons why we
didn’t attempt this photograph:
•
•
•
Most of the features of interest
are saturated.
Different blur kernels for
different lights (compare lantern
streaks with sky light streaks
and different than the water
reflection streaks, and the car
streaks below bridge and the
streaks to left of bridge.)
The objects reflected by flash
are stationary and have no
motion blur.
We don’t handle subject motion blur
Failure mode
Original photograph
Matlab’s deconvblind
Photoshop sharpen more
Our output
Blur kernel
Original photograph
Our output
Blur kernel
Original photograph
Our output
Blur kernel
Matlab’s deconvblind
Original photograph