MRF model-based identification of shift-variant point spread function for a class of imaging systems

Abstract

Identification of pointwise space-variant blur is an important area of research in image processing and computer vision. In this paper, we propose an MAP-MRF based scheme for identifying a class of shift-variant imaging systems whose point spread function can be parameterized by a single blur parameter. The space-variant (SV) blur parameter is modeled as an MRF and the MAP estimate of the SV blur parameter is obtained using simulated annealing. The scheme is amenable to the incorporation of smoothness constraints on the spatial variations of the blur parameter and simultaneously allows for inclusion of line fields to preserve discontinuities in the shift-variant blur. The performance of the proposed scheme is tested on synthetic as well as real data and the estimates of the SV blur are found to be better than that of the window-based blur identification technique.