Image restoration is the problem of recovering images which have been degraded by blurring and noise. Since imaging devices are never perfect, there are many applications for image restoration: astronomy, medical imaging, remote sensing, and microscopy are but a few.
Techniques for image restoration can be loosely grouped into two categories: local and global. Local filters restore an image one pixel at a time, using information from surrounding pixels. In global restoration techniques, each pixel contributes to the restoration of every other pixel. As a general rule (and there are exceptions), local filters are fast but do not yield very good results; global filters are slow but are capable of astonishingly good results.
In this thesis, a new approach to local image restoration is developed. This method is based on approximating functions of many variables on a multidimensional grid of points, hence the name Grid Filters. These filters generate excellent restoration results and are comparatively fast.