An adaptive optics scanning laser ophthalmoscope (AOSLO) has the characteristics of a high resolution and a small field of view (FOV), which are greatly affected by eye motion. Continual eye motion will cause distortions both within the frame (intra-frame) and between frames (inter-frame). Overcoming eye motion and achieving image stabilization is the first step and is of great importance in image analysis. Although cross-correlation-based methods enable image registration to be achieved, the manual identification and distinguishing of images with saccades is required; manual registration has a high accuracy, but it is time-consuming and complicated. Some imaging systems are able to compensate for eye motion during the imaging process, but special hardware devices need to be integrated into the system. In this paper, we proposed a deep-learning-based algorithm for automatic image stabilization. The algorithm used the VGG-16 network to extract convolution features and a correlation filter to detect the position of reference in the next frame, and finally, it compensated for displacement to achieve registration. According to the results, the mean difference in the vertical and horizontal displacement between the algorithm and manual registration was 0.07 pixels and 0.16 pixels, respectively, with a 95% confidence interval of (−3.26 px, 3.40 px) and (−4.99 px, 5.30 px). The Pearson correlation coefficients for the vertical and horizontal displacements between these two methods were 0.99 and 0.99, respectively. Compared with cross-correlation-based methods, the algorithm had a higher accuracy, automatically removed images with blinks, and corrected images with saccades. Compared with manual registration, the algorithm enabled manual registration accuracy to be achieved without manual intervention.

Refractive surgery is recognized as an effective method for myopia treatment, but it can induce night-vision disturbances such as glare. We present an eye modeling method for the optical quality assessment in response to the structural change of eyes by femto-LASIK surgery. Customized eye models were built from the measurements of 134 right eyes pre- and post-operatively. Optical performance was evaluated using spot diagrams, point spread functions (PSFs), modulation transfer functions (MTFs) and chromatic aberrations at various fields (0–30°), different pupil diameters (2–6 mm) and initial myopias (− 1.25 to − 10.5 D). Pupil size and initial myopia are the two major factors that affect visual performance of post-operative eyes. The results of spot diagrams, PSFs, and MTFs indicated that post-operative visual performance deteriorated as visual field and pupil size increased and it was significantly influenced by initial myopia. Post-operative chromatic aberrations were also affected by initial myopia, but tended to decrease as pupil size increased. The use of eye modeling for refractive surgery assessment could possibly provide a more personalized surgical approach, could improve the prediction accuracy of refractive surgery outcomes and aid the invention and development of better surgical methods.