Athermalization of infrared optical system through wavefront coding

Xie, Hao; Yongpeng, Su; Zhu, Meng; Yang, Lei; Wang, Shanshan; Wang, Xiaobo; Yang, Tong

doi:10.1016/j.optcom.2019.02.043

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…More particularly, our modality should be appropriate for the components with small diameter, and therefore the attempt to increase the object distance for improving the DOF is not available. Wave-front coding is a prevalent strategy used to appreciably improve the DOF without sacrificing the resolution [14,15,16], which combines a fabricated phase mask and digital signal processing in a fundamental manner [17,18,19]. Owing to the phase mask occupying the whole pupil aperture, and therefore power of propagating light and spatial resolution is completely preserved [20].…”

Section: Discussionmentioning

confidence: 99%

Development of a small-diameter and high-resolution industrial endoscope with CMOS image sensor

Yang

Chen

Zhu

et al. 2019

Sensors and Actuators A: Physical

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Section: Discussionmentioning

confidence: 99%

Development of a small-diameter and high-resolution industrial endoscope with CMOS image sensor

Yang

Chen

Zhu

et al. 2019

Sensors and Actuators A: Physical

Self Cite

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“…Stereo matching algorithms play vital roles in the fields of robotics and autonomous driving, realizing perceptional functionalities such as visual odometry [1] and object detection/tracking [2]- [5]. Compared to the active methods like LIDAR systems [6] and infrared systems [7], stereo matching algorithms are typically more cost-effective and versatile. A lot of research efforts, as summarized in [8], have been proposed on stereo matching techniques.…”

Section: Introductionmentioning

confidence: 99%

CLIF: Cross-Layer Information Fusion for Stereo Matching and its Hardware Implementation

Chen

Liu

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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The rapid advancement of intelligent systems, especially robotics and autonomous driving, is highly reliant on low-complexity and high-accuracy stereo matching algorithms. However, the performance of state-of-the-art stereo matching algorithms still has great space for improvement by gaining awareness of the implicit information hidden in the cost volume layers. In this paper, we propose a low-complexity local stereo matching algorithm named Cross-Layer Information Fusion (CLIF), to improve the matching accuracy by exploring the hidden information. First, we analyze and extract the hidden information into an auxiliary extractor using a novel fusion method. Second, we propose an information sharing strategy that transforms the extractor into a regularization term on each cost volume layer. Then we improve the design by re-constructing the information extractor between the adjacent cost volume layers and form a pipelined hardware architecture on the FPGA platform. Experimental results show that the proposed CLIF algorithm improves 6.53% average accuracy incurring negligible resources and performance impacts, compared to the state-ofthe-art solutions.

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“…They have strict requirements for size, weight and stability. Additionally, infrared imaging systems are sensitive to environmental temperature, which causes significant thermal defocus aberration and degrades imaging quality [1][2][3]. Conventional athermalization approaches such as the active mechanical [4], passive mechanical [5] and passive optical measures [6], have their disadvantages in size, weight and stability.…”

Section: Introductionmentioning

confidence: 99%

Effect of point spread function position bias in wavefront coding infrared imaging system

Feng

Shi

Liu

et al. 2020

J. Opt.

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By an experimental finding, for a given radiant point source and a given infrared detector sampling, an optical point spread function (PSF) falling on different positions of an infrared detector focal plane array, will result in different measured PSFs. Those measured PSFs known as decoding kernels will further produce different decoded images. Therefore, this work explores the effect of PSF position bias on decoded images in a wavefront coding infrared imaging system. This paper theoretically analyzes the effect of PSF position bias on decoded images. A simulation is conducted for qualitatively evaluating the effect of PSF position bias on decoded images by mean structural similarity (MSSIM) index. Simulated results prove that the probability for MSSIM index greater than 0.75 is 91.38%, and that for less than 0.5 is only 1.22% but this minority seriously degrades the decoded images. We construct an experimental setup for capturing the raw PSF image at a random position and proposes a procedure to reduce the noise of a raw PSF image. The experimental result demonstrates the feasibility for measuring a PSF to achieve a decoded image with good quality.

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Athermalization of infrared optical system through wavefront coding

Cited by 7 publications

References 24 publications

Development of a small-diameter and high-resolution industrial endoscope with CMOS image sensor

Development of a small-diameter and high-resolution industrial endoscope with CMOS image sensor

CLIF: Cross-Layer Information Fusion for Stereo Matching and its Hardware Implementation

Effect of point spread function position bias in wavefront coding infrared imaging system

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