Intensity-enhanced deep network wavefront reconstruction in Shack-Hartmann sensors.

The Shack-Hartmann wavefront sensor (SH-WFS) is known to produce incorrect measurements of the wavefront gradient in the presence of non-uniform illumination. Moreover, the most common least-squares phase reconstructors cannot accurately reconstruct the wavefront in the presence of branch points. We therefore developed the intensity/slopes network (ISNet), a deep convolutional-neural-network-based reconstructor that uses both the wavefront gradient information and the intensity of the SH-WFS's subapertures to provide better wavefront reconstruction.

Super-resolution retinal imaging using optically reassigned scanning laser ophthalmoscopy.

Super-resolution optical microscopy techniques have enabled the discovery and visualization of numerous phenomena in physics, chemistry and biology. However, the highest resolution super-resolution techniques depend on nonlinear fluorescence phenomena and are thus inaccessible to the myriad applications that require reflective imaging. One promising super-resolution technique is optical reassignment, which so far has only shown potential for fluorescence imaging at low speeds.

Statistical Models of Signal and Noise and Fundamental Limits of Segmentation Accuracy in Retinal Optical Coherence Tomography.

Optical coherence tomography (OCT) has revolutionized diagnosis and prognosis of ophthalmic diseases by visualization and measurement of retinal layers. To speed up the quantitative analysis of disease biomarkers, an increasing number of automatic segmentation algorithms have been proposed to estimate the boundary locations of retinal layers. While the performance of these algorithms has significantly improved in recent years, a critical question to ask is how far we are from a theoretical limit to OCT segmentation performance.