Robust grid registration for non-blind PSF estimation

Given a blurred image of a known test grid and an accurate estimate of the unblurred image, it has been demonstrated that the underlying blur kernel (or point-spread function, PSF) can be reliably estimated. Unfortunately, the estimate of the sharp image can be sensitive to common imperfections in the setup used to obtain the blurred image, and errors in the image estimate result in an unreliable PSF estimate. We propose a robust ad-hoc method to estimate a sharp prior image, given a blurry, noisy image of the test grid from Joshi taken in imperfect lab and lighting conditions. The proposed algorithm is able to reliably reject superfluous image content, can deal with spatially-varying lighting, and is insensitive to errors in alignment of the grid with the image plane. We demonstrate the algorithms performance through simulation, and with a set of test images. We also show that our grid registration algorithm leads to improved PSF estimation and deblurring, compared to an affine registration using spatially invariant lighting correction.