In this paper, we overview previously published works on the robustness of diffuser-based single random phase encoding (SRPE) lensless imaging system to sensor parameters such as pixel size and number of pixels. Lensless imaging systems are cheaper, more compact, and more portable than their lens-based counterparts due to the absence of expensive and bulky optical elements such as lenses. Our recent work has shown that the performance of an SRPE system does not suffer appreciably as we increase the pixel size of the sensor and reduce the number of pixels of the sensor. For example, we have shown that reducing the number of sensor pixels by orders of magnitude does not appreciably affect the deep neural network assisted classification accuracy of SRPE systems. Thus, providing many benefits in terms of data processing and storage. In addition, the lateral resolution of the SRPE system is robust to reducing the number of pixels of the sensor and increasing the pixel size. Our results indicate that SRPE systems may be more advantageous, compared to their lens-based counterparts, in computationally constrained environment.
KEYWORDS: Fiber optic gyroscopes, Object detection, Sensors, Active remote sensing, 3D image processing, Integral imaging, LIDAR, Thermal sensing, Education and training, 3D surface sensing
This paper presents an overview of a previously published work on the performance comparison of different sensors
(Visible, LWIR, and LiDAR-based imaging systems) for the task of object detection and classification in the presence of
degradation such as fog and partial occlusions. Three-dimensional integral imaging has been shown to improve the
detection accuracy of object detectors operating in both visible and LWIR domains. As fog affects the image quality of
different sensors in different ways, we have trained deep learning detectors for each sensor for 2D imaging as well as 3D
integral imaging to compare the performance of sensors in the presence of degradation such as fog and partial occlusions.
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