Numerical Analysis of Micro-Lens Array to the Mid-IR Range

New interferometric IR techniques have recently been developed to allow Sun-Jupiter-like detections in deep space. These techniques demand a high angular resolution, a high sensitivity towards signal detection buried in noise, and a well-defined bandwidth of spectral resolution. Micro-lens arrangements have helped increase the use of these parameters for IR detectors. In this paper we present a finite element method (FEM)-based simulation of a typical micro-lens array, to be used in mid-IR cameras, where the aperture geometry and radius of curvature are varied for design optimization. Moreover, we show the spot and optical aberrations produced by two types of geometrical arrangements. This procedure could be helpful in improving the IR detector signal in the exoplanets exploration, in systems placed outside of the earth’s atmosphere.

Aberration analysis of AOTF-based stereoscopic spectral imager using optical design software

Stereoscopic spectral imagers using acousto-optic tunable filters (AOTF) provide high-resolution narrow band images acquired from two viewpoints with different polarization in arbitrary spectral intervals, which allows obtaining three-dimensional hyperspectral models of inspected objects for many applications. We discuss modeling of acousto-optic (AO) cell for optical system design and introduce a program module for ray tracing through AO cell compatible with Zemax optical design software. A detailed study of the optical aberrations that limit the image quality in two AOTF-based stereoscopic systems implementing simultaneous AO diffraction of two differently polarized beams in single AO cell is presented. This approach may be used to design various AOTF-based imaging systems, but the limitations of ray tracing analysis should be considered.

Optical Aberrations Correction in Postprocessing Using Imaging Simulation

As the popularity of mobile photography continues to grow, considerable effort is being invested in the reconstruction of degraded images. Due to the spatial variation in optical aberrations, which cannot be avoided during the lens design process, recent commercial cameras have shifted some of these correction tasks from optical design to postprocessing systems. However, without engaging with the optical parameters, these systems only achieve limited correction for aberrations. In this work, we propose a practical method for recovering the degradation caused by optical aberrations. Specifically, we establish an imaging simulation system based on our proposed optical point spread function model. Given the optical parameters of the camera, it generates the imaging results of these specific devices. To perform the restoration, we design a spatial-adaptive network model on synthetic data pairs generated by the imaging simulation system, eliminating the overhead of capturing training data by a large amount of shooting and registration. Moreover, we comprehensively evaluate the proposed method in simulations and experimentally with a customized digital-single-lens-reflex camera lens and HUAWEI HONOR 20, respectively. The experiments demonstrate that our solution successfully removes spatially variant blur and color dispersion. When compared with the state-of-the-art deblur methods, the proposed approach achieves better results with a lower computational overhead. Moreover, the reconstruction technique does not introduce artificial texture and is convenient to transfer to current commercial cameras. Project Page: https://github.com/TanGeeGo/ImagingSimulation .

On the Relationship between Corneal Biomechanics, Macrostructure and Optical Properties

Optical properties of the cornea are responsible for correct vision, ultrastructure allows optical transparency and biomechanical properties governs the shape, elasticity or stiffness of the cor-nea affecting ocular integrity and intraocular pressure. Therefore, optical aberrations, corneal transparency, structure and biomechanics play a fundamental role in the optical quality of hu-man vision, ocular health and refractive surgery outcomes. However, the convergence of those properties is not yet reported at macroscopic scale within the hierarchical structure of the cornea. This work explores the relationships between biomechanics, structure and optical properties (corneal aberrations and optical density) at macrostructural level of the cornea through dual Placido-Scheimpflug imaging and air-puff tonometry systems in a healthy young adult popula-tion. Results showed convergence between optical transparency, corneal macrostructure and biomechanics.

Impact of Windshield Optical Aberrations on Visual Range Camera Based Classification Tasks Performed by CNNs

Cameras operating in the visual range of the electromagnetic spectrum are central to advanced driver assistance systems (ADAS). Front cameras, analyzing traffic, are often located behind the windshield to detect and classify objects.Thus, the area of the windshield within the camera’s field of view is a part of the optical system. Simple windshields consist of two curved glass surfaces connected by a thermoplastic interlayer. Due to defects present in the raw glass, as well as those introduced during the bending and lamination process, windshields will have optical aberrations. While optical quality may be suitable for human vision, it can fall short of what is needed for machine vision. In this article we investigate how the optical aberrations generated by laminated safety glass (LSG) influence the optical performance of a camera system and based on this, how the classification of image content by a convolutional neural network (CNN) is affected. A method for wavefront measurements of LSG samples is presented, which allows us to parameterize a linear optical model in Zernike Space. From this, we derive space-variant point spread functions (PSFs) and apply those to the dataset to simulate the windshield’s impact on the camera image. As a use case, a CNN was trained on the unmodified dataset and compared to the modified versions with the LSG models applied. We measured and modelled two different LSG samples, one with high and the other one with low optical quality. We compare the prediction accuracy of the classification with the unmodified data. The highquality sample had negligible effect on the overall classification accuracy, while the low-quality sample lowered the prediction accuracy by up to ten percentage points due to the optical aberrations.

Spatial recall index for machine learning algorithms

We present a novel metric Spatial Recall Index to assess the performance of machine-learning (ML) algorithms for automotive applications, focusing on where in the image which performance occurs. Typical metrics like intersection-over-union (IoU), precisionrecallcurves or average precision (AP) quantify the performance over a whole database of images, neglecting spatial performance variations. But as the optics of camera systems are spatially variable over the field of view, the performance of ML-based algorithms is also a function of space, which we show in simulation: A realistic objective lens based on a Cooke-triplet that exhibits typical optical aberrations like astigmatism and chromatic aberration, all variable over field, is modeled. The model is then applied to a subset of the BDD100k dataset with spatially-varying kernels. We then quantify local changes in the performance of the pre-trained Mask R-CNN algorithm. Our examples demonstrate the spatial dependence of the performance of ML-based algorithms from the optical quality over field, highlighting the need to take the spatial dimension into account when training ML-based algorithms, especially when looking forward to autonomous driving applications.

Computer Simulation for the Effects of Optical Aberrations on Solar Images Using Karhunen-Loeve polynomials

Numerical simulations were carried out to evaluate the effects of different aberrations modes on the performance of optical system, when observing and imaging the solar surface. Karhunen-Loeve aberrations modes were simulated as a wave front error in the aperture function of the optical system. To identify and apply the appropriate rectification that removes or reduces various types of aberration, their attribute must be firstly determined and quantitatively described. Wave aberration function is well suitable for this purpose because it fully characterizes the progressive effect of the optical system on the wave front passing through the aperture. The Karhunen-Loeve polynomials for circular aperture were used to describe wave front deviations and to predict the initial state of adaptive optics corrections. The results showed that increasing the aberration modes causes an increase in the blurring of the observed image. Also, we conclude that the optical phase error is increased significantly when aperture’s radii are increased.

Accurate localization microscopy by intrinsic aberration calibration

A standard paradigm of localization microscopy involves extension from two to three dimensions by engineering information into emitter images, and approximation of errors resulting from the field dependence of optical aberrations. We invert this standard paradigm, introducing the concept of fully exploiting the latent information of intrinsic aberrations by comprehensive calibration of an ordinary microscope, enabling accurate localization of single emitters in three dimensions throughout an ultrawide and deep field. To complete the extraction of spatial information from microscale bodies ranging from imaging substrates to microsystem technologies, we introduce a synergistic concept of the rigid transformation of the positions of multiple emitters in three dimensions, improving precision, testing accuracy, and yielding measurements in six degrees of freedom. Our study illuminates the challenge of aberration effects in localization microscopy, redefines the challenge as an opportunity for accurate, precise, and complete localization, and elucidates the performance and reliability of a complex microelectromechanical system.