Seminar at SMU Delhi

Blurring of photographic images due to camera shake is quite common, and recovering the underlying image from such photographs is an interesting inference problem. Ignoring rotations, the blurring process can be modeled as a convolution of the underlying image and a “blur kernel” or “point spread function”, and the problem is thus referred to as “deconvolution”. The problem is well-studied when the blur kernel is known. However, non-blind deconvolution, when the blur kernel is unknown, is more difficult. Considerable progress in this problem has been made during the last decade by making `natural' assumptions about the unknown image in the form of a prior. In this talk, we describe a generalization of the commonly used prior family, and discuss how existing estimation methods can be adapted to use it.