TJHSST Computer Systems Lab Senior
Research Project Image Deblurring 2007-2008
Peter Chapman
November 2, 2007
Abstract
Either for recreation or for work, everyone has taken pictures be-
fore; inevitably with taking photographs, everyone’s had blurry im-
ages. Whether, the target of the image moved, the camera itself
moved, or the lens was simply out of focus, blurry images are of-
ten useless. The common misconception is that it is impossible to
”deblur” a blurry image. In reality, if you know a little information
about how the image was blurred, or can predict how the image was
blurred, it is possible do some repair on the image. The restoration
of blurred images can be applied to everything from vacation files to
photographs taken by law enforcement in order to identify a license
plate or even a face. A program that can adequately restore these
images would be invaluable.
Keywords: Fourier Transformations, Spacial Domain, Frequency
Domain, Phase Domain, Deconvolution, Fast Fourier Transforma-
tion, Cooley-Turkey Fast Fourier Transformation Algorithm, Discrete
Fourier Transformation
1 Introduction
1.1 Scope of Study
The image de-blurring process is relatively simple, but extremely difficult to
perform correctly. Although there are many variables involved when doing
the deblurring process, the most important is how the original image was
blurred. Questions such as what type of blurring the image is suffering from
1
whether it?s motion blur or not properly focused, and what direction or di-
rections the blur is in, must be answered adequately in order to perform
the process correctly. These issues can either be addressed by the program
automatically in an extremely difficult process known as Automatic Decon-
volution or by the user. Due to the complexity of Automatic Deconvolution
and the time constraints on this project, I will not attempt an in-depth
automatic deconvolution, but rather have such variables chosen by the user.
The final product will load any popular type of image, and after prompt-
ing the user fo