Current research is targeting the estimation and correction of lens imperfections. often modeled as a set of spatially varying point spread functions (PSFs). One way to measure these PSFs is their calibration with checkerboard patterns. Previous work, however, does not fully exploit all benefits of using a checkerboard. In particular, we show in this paper that the pose of the checkerboard with respect to the camera can be exploited to yield information on the circle of confusion, and thus the image blur of an ideal camera. By removing this expected blur, we can estimate residual PSFs that are due to the deviation of the optical system from a thin-lens model. The residual PSFs can then be used to sharpen images at comparable lens settings. Practical side effects of our method are the design of a self-identifying pattern that can be robustly detected even in the case of image blur, and a corresponding algorithm for its detection.