Seminar at SMU Delhi

July 17, 2015 (Friday) , 3:30 PM at Webinar
Speaker: Kaustav Nandy, Indian Statistical Institute, Delhi
Title: Deconvolution using natural image priors
Abstract of Talk
Blurring of images is a common phenomenon, typical examples being astronomical images that may be degraded due to atmospheric factors or telescope optics, and photographs that may be blurred due to motion of the subject or camera shake during relatively long exposures. Recovering the underlying image from an observed blurred image is an interesting inference problem. As the blurring process is modeled as a convolution of the underlying image and a `blur kernel' or `point spread function', this problem is usually referred to as the deconvolution problem. The problem is easier to solve when the blur kernel is known, as is typical in astronomical images, and reasonable solutions have been available for several decades. A more difficult version of the problem is blind deconvolution, where the blur kernel is unknown, as in photographs of natural scenes. Considerable progress has been made recently by using priors on the space of natural images. In this talk, we give an overview of the deconvolution problem and present some preliminary work in trying to understand the blind deconvolution problem and hopefully obtain better solutions than those currently available.