Intraocular lenses (IOLs) are commonly implanted after surgical removal of a cataractous lens. A variety of IOL materials are currently available, including collamer, hydrophobic acrylic, hydrophilic acrylic, PHEMA copolymer, polymethylmethacrylate (PMMA), and silicone. High-quality polymers with distinct physical and optical properties for IOL manufacturing and in line with the highest quality standards on the market have evolved to encompass medical needs. Each of them and their packaging show unique advantages and disadvantages. Here, we highlight the evolution of polymeric materials and mainly the current state of the art of the unique properties of some polymeric systems used for IOL design, identifying current limitations for future improvements. We investigate the characteristics of the next generation of IOL materials, which must satisfy biocompatibility requirements and have tuneable refractive index to create patient-specific eye power, preventing formation of posterior capsular opacification.

Adaptive optics (AO) is employed for the continuous measurement and correction of ocular aberrations. Human eye refractive errors (lower-order aberrations such as myopia and astigmatism) are corrected with contact lenses and excimer laser surgery. Under twilight vision conditions, when the pupil of the human eye dilates to 5–7 mm in diameter, higher-order aberrations affect the visual acuity. The combined use of wavefront (WF) technology and AO systems allows the pre-operative evaluation of refractive surgical procedures to compensate for the higher-order optical aberrations of the human eye, guiding the surgeon in choosing the procedure parameters. Here, we report a brief history of AO, starting from the description of the Shack–Hartmann method, which allowed the first in vivo measurement of the eye’s wave aberration, the wavefront sensing technologies (WSTs), and their principles. Then, the limitations of the ocular wavefront ascribed to the IOL polymeric materials and design, as well as future perspectives on improving patient vision quality and meeting clinical requests, are described.

A A A Ab b b bs s s st t t tr r r ra a a ac c c ct t t t: In this paper we present two methods for non-uniformity correction of imaging array detectors based on neural networks, both of them exploit image properties to supply lack of calibrations and maximize the entropy of the output. The first method uses a self-organizing net that produces a linear correction of the raw data with coefficients that adapt continuously. The second method employs a kind of contrast equalization curve to match pixel distributions. Our work originates from Silicon detectors but the treatment is general enough to be applicable to many kinds of array detectors like those used in Infrared imaging or in high energy physics. Purpose of this paper is to show that itÕs possible to calculate pixel parameters without explicit calibrations substituting the lacking information with some hypotheses on incoming data. A simple example is this: if one knows that pixel A always receives the same flux of pixel B, then, after a few images, one can get the relative calibration of A and B: the knowledge that A and B must see the same partly replaces information that would come from a calibration.We will present two different methods, based on two different sets of hypotheses. Our first method make the hypothesis that images arriving on the detector follow a Gibbs 1 mbh@trieste.infn.it, http://www.ts.infn.it/~mbh/MBHgeneral.html, renato@mediastudio.it