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In this work, a new approach, a method using artificial neural networks was applied to reconstruct the wavefront. First, the optimal structure of neural networks was found. Then, the networks were trained on both noise-free and noisy spot patterns. The results of the wavefront reconstruction with artificial neural networks were compared to those obtained through the least square fit and singular value decomposition method.

Collagen, as the most abundant protein in the human body, determines the unique physiological and optical properties of the connective tissues including cornea and sclera. The ultrastructure of collagen, which conventionally can only be resolved by electron microscopy, now can be probed by optical second harmonic generation (SHG) imaging. SHG imaging revealed that corneal collagen fibrils are regularly packed as a polycrystalline lattice, accounting for the transparency of cornea. In contrast, scleral fibrils possess inhomogeneous, tubelike structures with thin hard shells, maintaining the high stiffness and elasticity of the sclera.

We describe what we believe to be the first wave-front measurements of the human eye at a sampling rate of 300 Hz with a custom Hartmann–Shack wave-front sensor that uses complementary metal-oxide semiconductor (CMOS) technology. This sensor has been developed to replace standard charge-coupled device (CCD) cameras and the slow software image processing that is normally used to reconstruct the wave front from the focal-plane image of a lenslet array. We describe the sensor’s principle of operation and introduce the performance with static wave fronts. The system has been used to measure human-eye wave-front aberrations with a bandwidth of 300 Hz, which is approximately an order of magnitude faster than with standard software-based solutions. Finally, we discuss the measured data and consider further improvements to the system.