Removing Photographic Blur Caused by Camera Motion: How Can you Identify When an Image Looks Blurred?
ruins many photographs. Conventional blind deconvolution methods
typically assume frequency domain constraints on images, or overly
simplied parametric forms for the motion path during camera shake.
Real camera motions can follow convoluted paths, and a spatial domain
prior can better maintain visually salient image characteristics. We
introduce a multi-scale method to remove the effects of camera shake
from seriously blurred images, by estimating the most probable blur
and original image using a variational approximation to the posterior
probability, and assuming a heavy-tailed distribution for bandpassed
image statistics. Our method assumes a uniform camera blur over the
image, negligible in-plane camera rotation, and no blur caused by
moving objects in the scene. The algorithm operator specifies an
image region without saturation effects within which to estimate the
blur kernel. I'll discuss issues in this blind deconvolution problem,
and show results for a variety of digital photographs.
Invitation to submit examples: I invite audience members to submit
examples of motion-blurred photographs to me a few days ahead of time.
I'll show the images you submit, and the result of our algorithm
applied to them. If you have a favorite blind deconvolution or
restoration algorithm, please apply it to your image and send it and
I'll show that, too.
Joint work with: Rob Fergus, Barun Singh, both from MIT CSAIL, and
Aaron Hertzman and Sam Roweis, both from the University of Toronto.