发帖
雷达卡
摘要翻译：
训练自动驾驶车辆需要在所有情况下的大量驾驶序列\引用{zhao2016}。通常，模拟环境（software-in-the-loop,SiL）伴随着真实世界的测试驱动器，以系统地改变环境参数。在这些SiL模拟的光学模型中，一个缺失的部分是线性系统理论中由光学系统的点扩展函数(PSF)给出的清晰度。我们提出了一种新的光学系统PSF的数值模型，它可以有效地模拟PSF的实验测量和透镜设计模拟。该模型的数值基础是用人工神经网络(ANN)对PSF进行非线性回归。该模型的新颖之处在于它的可移植性和参数化，这使得该模型基本上可以应用于任何可以想象的光学模拟场景，例如将一个被测量的透镜插入计算机游戏中来训练自动车辆。我们给出了一个透镜测量系列，得到了PSF的数值函数，该函数只依赖于离焦、场和方位角参数。通过将现有的图像和视频与此PSF模型进行卷积，我们将被测透镜作为传递函数，从而生成一个像是用被测透镜本身看到的图像。该方法适用于任何光学场景，但我们将重点放在自动驾驶的背景下，在这种背景下，检测算法的质量直接依赖于所使用的摄像机系统的光学质量。通过光学模型的参数化，我们提出了一种基于产品实际使用的真实测量镜头来验证基于相机的ADAS的功能和安全极限的方法。
---
英文标题：
《Realistic Image Degradation with Measured PSF》
---
作者：
Christian Wittpahl, Hatem Ben Zakour, Matthias Lehmann, Alexander
Braun
---
最新提交年份：
2018
---
分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Image and Video Processing 图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
--
一级分类：Physics 物理学
二级分类：Instrumentation and Methods for Astrophysics 天体物理学仪器和方法
分类描述：Detector and telescope design, experiment proposals. Laboratory Astrophysics. Methods for data analysis, statistical methods. Software, database design
探测器和望远镜设计，实验建议。实验室天体物理学。资料分析方法，统计方法。软件，数据库设计
--
---
英文摘要：
Training autonomous vehicles requires lots of driving sequences in all situations\cite{zhao2016}. Typically a simulation environment (software-in-the-loop, SiL) accompanies real-world test drives to systematically vary environmental parameters. A missing piece in the optical model of those SiL simulations is the sharpness, given in linear system theory by the point-spread function (PSF) of the optical system. We present a novel numerical model for the PSF of an optical system that can efficiently model both experimental measurements and lens design simulations of the PSF. The numerical basis for this model is a non-linear regression of the PSF with an artificial neural network (ANN). The novelty lies in the portability and the parameterization of this model, which allows to apply this model in basically any conceivable optical simulation scenario, e.g. inserting a measured lens into a computer game to train autonomous vehicles. We present a lens measurement series, yielding a numerical function for the PSF that depends only on the parameters defocus, field and azimuth. By convolving existing images and videos with this PSF model we apply the measured lens as a transfer function, therefore generating an image as if it were seen with the measured lens itself. Applications of this method are in any optical scenario, but we focus on the context of autonomous driving, where quality of the detection algorithms depends directly on the optical quality of the used camera system. With the parameterization of the optical model we present a method to validate the functional and safety limits of camera-based ADAS based on the real, measured lens actually used in the product.
---
PDF链接：
https://arxiv.org/pdf/1801.02197
京公网安备 11010802022788号
