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Multispectral simulation environment for modeling low-light-level sensor systems

机译：用于模拟微光传感器系统的多光谱仿真环境

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Abstract: Image intensifying cameras have been found to be extremely useful in low-light-level (LLL) scenarios including military night vision and civilian rescue operations. These sensors utilize the available visible region photons and an amplification process to produce high contrast imagery. It has been demonstrated that processing techniques can further enhance the quality of this imagery. For example, fusion with matching thermal IR imagery can improve image content when very little visible region contrast is available. To aid in the improvement of current algorithms and the development of new ones, a high fidelity simulation environment capable of producing radiometrically correct multi-band imagery for low- light-level conditions is desired. This paper describes a modeling environment attempting to meet these criteria by addressing the task as two individual components: (1) prediction of a low-light-level radiance field from an arbitrary scene, and (2) simulation of the output from a low- light-level sensor for a given radiance field. The radiance prediction engine utilized in this environment is the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model which is a first principles based multi-spectral synthetic image generation model capable of producing an arbitrary number of bands in the 0.28 to 20 micrometer region. The DIRSIG model is utilized to produce high spatial and spectral resolution radiance field images. These images are then processed by a user configurable multi-stage low-light-level sensor model that applies the appropriate noise and modulation transfer function (MTF) at each stage in the image processing chain. This includes the ability to reproduce common intensifying sensor artifacts such as saturation and 'blooming.' Additionally, co-registered imagery in other spectral bands may be simultaneously generated for testing fusion and exploitation algorithms. This paper discusses specific aspects of the DIRSIG radiance prediction for low- light-level conditions including the incorporation of natural and man-made sources which emphasizes the importance of accurate BRDF. A description of the implementation of each stage in the image processing and capture chain for the LLL model is also presented. Finally, simulated images are presented and qualitatively compared to lab acquired imagery from a commercial system. !16
机译：摘要：已经发现图像增强相机在微光（LLL）场景中非常有用，包括军事夜视和民用救援行动。这些传感器利用可用的可见区域光子和放大过程来产生高对比度图像。已经证明，处理技术可以进一步提高该图像的质量。例如，当很少有可见区域对比度可用时，与匹配的热红外图像融合可以改善图像内容。为了帮助改进当前算法和开发新算法，需要一种能够针对低光水平条件产生辐射精确的多波段图像的高保真度仿真环境。本文介绍了一个建模环境，该环境通过将任务作为两个独立的组件来解决，试图满足这些标准：（1）从任意场景预测低光照辐射场，以及（2）模拟低照度的输出给定辐射场的光度传感器。在这种环境中使用的辐射预测引擎是数字成像和遥感图像生成（DIRSIG）模型，该模型是基于第一原理的多光谱合成图像生成模型，能够在0.28至20微米范围内产生任意数量的波段。 DIRSIG模型用于产生高空间和光谱分辨率的辐射场图像。然后，由用户可配置的多级微光传感器模型处理这些图像，该模型在图像处理链的每个阶段都应用适当的噪声和调制传递函数（MTF）。这包括再现常见的增强传感器伪像（例如饱和度和“起霜”）的能力。另外，可以同时生成其他光谱带中的配准图像，以测试融合和开发算法。本文讨论了在低光照条件下DIRSIG辐射预测的特定方面，包括自然和人工光源的结合，强调了精确BRDF的重要性。还介绍了LLL模型的图像处理和捕获链中每个阶段的实现方式。最后，将呈现仿真图像并将其与从商业系统获得的实验室图像进行定性比较。 ！16

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