Transcript Image Deblurring

Vincent DeVito
Computer Systems Lab
2009-2010
The goal of my project is to take an image
input, artificially blur it using a known blur
kernel, then using deconvolution techniques to
deblur and restore the image, then run a last
step to reduce the noise of the image. The goal
is to have the input and output images be
identical with a blurry intermediate image.
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Running goal for image processors and photo
editors
Many methods of deconvolution exist
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Many utilize the Fourier Transform
Current progress focused on blur kernel
estimation
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Better kernel  more accurate, clear output image
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The group of Lu Yuan, et al. designed project
with blurry/noisy image pairs
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Blurry image intensity + noisy image sharpness +
deconvolution = sharp, deblurred output image
The group of Rob Fergus, et al. designed
project to estimate blur kernel from naturally
blurred image
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A few inputs + kernel estimation algorithm +
deconvolution = deblurred output image with few
artifacts
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Photography
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Improve image quality
Restore image
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Machine Vision
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Requires input images to be of good clarity
Blur could ruin techniques such as edge detection
Intermediate step
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Basic image processing techniques (HIPR2
online worksheets)
Pointwise operations, geometric operations,
morphology
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First version
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Clear input  artificial blurring using known blur
kernel  deconvolution techniques using same
kernel  reduce noise  output image
Hope to have the output image be as clear and sharp
as the original input image
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Final Version (hopefully)
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Naturally blurred input  estimation of unknown
blur kernel  deconvolution techniques using that
kernel  reduce noise  output image
Hope to have the output image be a clear, sharp
version of the blurry input image