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Unsupervised correction of relative longitudinal aberrations for multispectral imaging using a multiresolution approach

Longitudinal aberrations appear in multispectral cameras featuring a monochrome sensor with several optical filters in front of it. Due to the slightly different optical properties of the filters, the focal lengths are different and the images cannot remain sharp for all the color channels. We seek for an unsupervised correction of these aberrations, relative to a given reference color channel. "Unsupervised" means here that no calibration of the system is needed. We use a multiresolution approach that takes advantage of the high contrast present in the reference channel and that utilizes this information for the other, more blurred channels. The results of this correction are evaluated using the sharpness of the corrected image with respect to the original blurred image and using the color accuracy: an algorithm that would corrupt the spectral information of multispectral images would not be helpful. Moreover, using the original image and the one corrected with the algorithm, we can calculate the point spread function of the longitudinal aberrations. We then compare it to the point spread function obtained with another method which is based on the capture of a noise chart and thus requires calibration.