ABSTRACT
In this chapter, the authors discuss possible sources of spatially varying blur, such as defocus, camera motion, or object motion. They review known approaches to blur estimation, illustrate their performance on experiments with real data and indicate problems that must be solved to be applicable in super-resolution algorithms. The authors consider only algorithms working with multiple acquisitions – situations where they fuse information from several images to get an image of better resolution. They discuss possible sources of spatially varying blur, such as defocus, camera motion, or object motion. The effective resolution of an imaging system is limited not only by the physical resolution of an image sensor but also by blur. The authors assume that the blur is approximately space-invariant inside objects, and the point-spread function can be represented by a set of convolution kernels for each object and a corresponding set of object contours.
