An Improved Schematic Human Eye Model for Human Vision Simulation

Wei Wang, Yong Yue
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Abstract

The human eye is a complex and precise optical imaging system. The refractive surgeries, e.g. LASIK, aim to correct the refractive error of the human eye. An important precondition of refractive surgeries is to model the human eye. The human eye model is used to simulate the human vision to guide and evaluate refractive surgeries. Especially, the defocus blur that is caused by the refractive error is considered in the human vision simulation. However, the asymmetry and irregularity of eyeball shape and optical media make it impossible to model the human eye accurately. This inaccuracy in the modelling of the human eye makes it is difficult to perform the surgeries accurately with several surgical complications, e.g. the halo and vision acuity in dark environments. In addition, the current studies of human eye modelling are for the human eye with normal vision. The main reason is the complex causes of pathological vision in pathological human eye modelling, e.g. the abnormal length of the optical axis and the abnormal dioptre of the cornea in myopia and hyperopia. This study proposes an improved schematic human eye model to achieve human eye modelling and simulation. The different length of the optical axis and the change of corneal thickness are considered in this human eye model. The main contributions of this study are to achieve the personalised modelling of the human eye with pathological vision and provide the visual quality evaluation of refractive surgeries.
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一种用于人眼视觉仿真的改进原理图模型
人眼是一个复杂而精密的光学成像系统。屈光手术,如LASIK,旨在矫正人眼的屈光不正。屈光手术的一个重要前提是模拟人眼。人眼模型用于模拟人眼视觉,指导和评估屈光手术。在人眼视觉模拟中,特别要考虑屈光不正引起的离焦模糊。然而,由于眼球形状和光学介质的不对称性和不规整性,人眼的精确建模是不可能的。这种人眼模型的不准确性使得精确地进行手术变得困难,并伴有一些手术并发症,例如黑暗环境中的光晕和视力。另外,目前的人眼建模研究都是针对正常视力的人眼进行的。其主要原因是病理性人眼建模中病理视觉的成因复杂,如近视和远视中光轴长度异常、角膜屈光度异常等。本文提出一种改进的原理图人眼模型,实现人眼的建模与仿真。该人眼模型考虑了光轴长度的不同和角膜厚度的变化。本研究的主要贡献在于实现病理性视力人眼的个性化建模,并提供屈光手术的视觉质量评估。
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Neurons identification of single-photon wide-field calcium fluorescent imaging data CTISC 2020 List Reviewer Page CTISC 2020 Commentary Image data augmentation method based on maximum activation point guided erasure Sponsors and Supporters: CTISC 2020
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