We propose a method to calibrate viewpoint-dependent, channel-wise image blur of near-eye displays, especially of Optical See-Through Head-Mounted Displays (OST-HMDs). Imperfections in HMD optics cause channel-wise image shift and blur that degrade the image quality of the display at a user's viewpoint. If we can estimate such characteristics perfectly, we could mitigate the effect by applying correction techniques from the computational photography in computer vision as analogous to cameras. Unfortunately, directly applying existing calibration techniques of cameras to OST-HMDs is not a straightforward task. Unlike ordinary imaging systems, image blur in OST-HMDs is viewpoint-dependent, i.e., the optical characteristic of a display dynamically changes depending on the current viewpoint of the user. This constraint makes the problem challenging since we must measure image blur of an HMD, ideally, over the entire 3D eyebox in which a user can see an image. To overcome this problem, we model the viewpoint-dependent blur as a Gaussian Light Field (GLF) that stores spatial information of the display screen as a (4D) light field with depth information and the blur as point-spread functions in the form of Gaussian kernels, respectively. We first describe both our GLF model and a calibration procedure to learn a GLF for a given OST-HMD. We then apply our calibration method to two HMDs that use different optics: a cubic prism or holographic gratings. The results show that our method achieves significantly better accuracy in Point-Spread Function (PSF) estimations with an accuracy about 2 to 7 dB in Peak SNR.
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