IEEE Transactions on Pattern Analysis and Machine Intelligence
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
ACM SIGGRAPH 2008 papers
Two-phase kernel estimation for robust motion deblurring
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
High-quality computational imaging through simple lenses
ACM Transactions on Graphics (TOG)
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All lenses have optical aberrations which reduce image sharpness. These aberrations can be reduced by deconvolving an image using the lens point spread function (PSF). However, fully measuring a PSF is laborious and prohibitive. Alternatively, one can simulate the PSF if the lens model is known. However, due to manufacturing tolerances lenses differ subtly from their models, so often a simulated PSF is a poor match to measured data. We present an algorithm that uses a PSF measurement at a single depth to calibrate the nominal lens model to the measured PSF. The calibrated model can then be used to compute the PSF for any desired setting of lens parameters for any scene depth, without additional measurements or calibration. The calibrated model gives deconvolution results comparable to measurement but is much more compact and require hundreds of times fewer calibration images.