Automated montaging and contrast stretching of adaptive optics ophthalmoscope images

We present an automated montaging method for distortion-corrected single- and multi-channel adaptive optics ophthalmoscope images. The approach employs a zero-mean normalized weighted cross-correlation (ZNWCC) that enables assigning real-valued pixel weights to account for irregular image boundaries, spatially varying signal-to-noise ratio, and motion of retinal features. An initial sub-pixel pairwise registration using translation, rotation, and, if needed, scaling is refined with a global registration quality metric derived from peak ZNWCC values. The images are montaged through weighted averaging and contrast stretching that equalize mean image brightness and contrast in overlapping regions, compensating for differences in overall illumination, detector offset, and detector gain. A final montage is created by combining the transformed images calculated through a single interpolation to mitigate loss of resolution, and with an optional polynomial field-flattening step to increase contrast throughout the montage. The method is demonstrated on AO retinal image sets from human and mouse eyes.