Abstract. The aged human eye is commonly affected by presbyopia, and therefore, it gradually loses its capability to form images of objects placed at different distances. Extended depth of focus (EDOF) imaging elements can overcome this inability, despite the introduction of a certain amount of aberration. This paper evaluates the EDOF imaging performance of the so-called peacock eye phase diffractive element, which focuses an incident plane wave into a segment of the optical axis and explores the element's potential use for ophthalmic presbyopia compensation optics. Two designs of the element are analyzed: the single peacock eye, which produces one focal segment along the axis, and the double peacock eye, which is a spatially multiplexed element that produces two focal segments with partial overlapping along the axis. The performances of the peacock eye elements are compared with those of multifocal lenses through numerical simulations as well as optical experiments in the image space. The results demonstrate that the peacock eye elements form sharper images along the focal segment than the multifocal lenses and, therefore, are more suitable for presbyopia compensation. The extreme points of the depth of field in the object space, which represent the remote and the near object points, have been experimentally obtained for both the single and the double peacock eye optical elements. The double peacock eye element has better imaging quality for relatively short and intermediate distances than the single peacock eye, whereas the latter seems better for far distance vision.

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Accurate endothelial cell density with specular microscopy is essential for correct clinical assessment of the cornea. Commercial specular microscopes incorporate automated cell segmentation methods to estimate cell density. However, these methods are prone to false cell detections in pathological corneas. This project aims to obtain a reliable automated cell density from specular microscopy images of both healthy and pathological corneas with convolutional neural networks. Convolutional neural networks require labeled datasets. Thus, we developed custom software for producing a curated dataset of labeled ground-truth images and cell density maps. In this paper, we implemented a fully convolutional regression network to predict the cell density map from the input microscopy image. Encouraging preliminary results show the potential of the method. This approach may pave the way for dealing with the variability of corneal endothelial cell images.

In this work we analyse the generation of a diffractive optical element (DOE) consisting of a multifocal Fresnel lens by means of an LCoS (liquid cristal on silicon) spatial light modulator (SLM). The multifocal lens is composed of a set of lenses of different focal length that share a common optical axis (coaxial combination) or have different axes in parallel (multi-axis combination). For both configurations, we present several ways to combine the phase distributions for three lenses with different focal lengths (f 1 , f 2 , f 3 ), into a single-phase distribution addressed to the SLM. Numerical simulations were carried out along with the experimental analysis to corroborate the results.

The key to accurate 3D shape measurement in Fringe Projection Profilometry (FPP) is the proper calibration of the measurement system. Current calibration techniques rely on phase-coordinate mapping (PCM) or back-projection stereo-vision (SV) methods. PCM methods are cumbersome to implement as they require precise positioning of the calibration target relative to the FPP system but produce highly accurate measurements within the calibration volume. SV methods generally do not achieve the same accuracy level. However, the calibration is more flexible in that the calibration target can be arbitrarily positioned. In this work, we propose a hybrid calibration method that leverages the SV calibration approach using a PCM method to achieve higher accuracy. The method has the flexibility of SV methods, is robust to lens distortions, and has a simple relation between the recovered phase and the metric coordinates. Experimental results show that the proposed Hybrid method outperforms the SV method in terms of accuracy and reconstruction time due to its low computational complexity.