Blind superresolving image recovery from blur-invariant edges
Kazuki Nishi, Shigeru Ando · 2002
The blind superresolution algorithm performs simultaneously the estimating task of a blur point spread function (PSF) and extrapolating task of the the graded resolution. In this paper, we apply this algorithm to an image recovery problem by using salient edge location information as a convex condition. On the first step, we extract blur-invariant image features, which actually are the steepest lines and points of isolated edges and corners. On the second step, we make a convex set from the blur-invariant image features and obtain a projection operator correspondent to it. By using this with other a prior information, we estimate and recover simultaneously the PSF and the original image through the well-known mathematical projection technique (POCS). Several simulation results are shown in comparison with the other method.>