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Several error measures are proposed, which quantify the inaccuracy of image deblurring using a particular incorrect PSF. Using a set of simulation results, it is shown that the desired metric is feasible even without specification of the unblurred prior image or the radiometric response of the camera. It is also shown that the proposed metric accurately and reliably predicts the resulting deblurring error from the use of an approximate PSF in place of an exact PSF.
In the work, raw CFA image data is acquired in conjunction with 3-axis acceleration data using a custom-built imaging system. The raw image data records the redistribution of light but is effected by camera motion and the rolling shutter mechanism. Through the use of acceleration data, the spread of light to neighboring pixels can be determined. We propose a new approach to jointly perform deblurring and demosaicking of the raw image. This approach adopts edge-preserving sparse prior in a MAP framework. The improvements brought by our algorithm is demonstrated by processing the data collected from the imaging system.
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