CS700:Graduate Seminar in Computer Science & Informatics

Computational Techniques for Image Deconvolution
(slides)

Powerful imaging devices (ranging from very large telescopes, to medical radiology, to modern microscopes) usually combine a device that collects light, or similar radiation measurements, with a computer that assembles the collected data into images that can be viewed by scientists and doctors. Although we want the recorded image to be a faithful representation of the true image scene, every image is more or less blurry. In image deblurring, the goal is to recover the original, sharp image by using a mathematical model of the blurring process. The key issue is that some information on the lost details is indeed present in the blurred image, but this ``hidden" information can be recovered only if we know the details of the blurring process. Because the blurring process is often modeled as a convolution equation, deblurring is often referred to as deconvolution. In this talk we describe computational techniques, including basic filtering approaches as well as state-of-the-art algorithms, that can be used for deconvolution.
Jim Nagy is Professor and the Director of Graduate Studies in the Department of Mathematics and Computer Science at Emory University. His research interest is in numerical linear algebra, scientific computation, numerical solutions to discrete ill-posed problems in signal and image processing.