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Optimal Filters from Calibration Data for Image Deconvolution with Data Acquisition Error

Abstract

Data acquisition errors due to dead pixels or other hardware defects can cause undesirable artifacts in imaging applications. Compensating for these defects typically requires knowledge such as a defective pixel map, which can be difficult or costly to obtain and which is not necessarily static. However, recent calibration data is readily available in many applications. In this paper, we compute optimal filters for image deconvolution with denoising using only this calibration data, by minimizing the empirical Bayes risk. We derive a bound on how the reconstruction changes as the number of dead pixels grows. We show that our approach is able to reconstruct missing information better than standard filtering approaches and is robust even in the presence of a large number of defects and to defects that arise after calibration.

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