Infrared Image Dynamic Range Compression and Contrast Enhancement Based on Optimal Mapping Curve

Zepeng, 吴泽鹏 Wu; Ming, 宣明 Xuan; Hong-guang, 贾宏光 Jia; Ming-chao, 朱明超 Zhu; Lingling, 郭玲玲 Guo; Hui, 刘慧 Liu

doi:10.3788/cjl201340.1209002

Cited by 2 publications

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“…In order to solve this problem, some scholars proposed a piecewise linear quantization method [2] , which uses different gains to map different sections, hoping to highlight specific grayscale intervals in the image. In order to adaptively select segmentation parameters, A Pardo et al [3] proposed to segment the image grayscale interval based on information entropy, and Wu Zepeng et al [4] proposed to segment the image grayscale interval based on the peaks and troughs in the grayscale histogram. segment.…”

Section: Methods Based On Linear Quantizationmentioning

confidence: 99%

Influence analysis of wide dynamic infrared image quantization on target detection performance

Jiang¹,

Cao

Shen³

et al. 2023

Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022)

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Deep learning-based object detection networks outperform the traditional detection methods. However, they lack interpretability and solid theoretical guidance. To guide and support the application of object detection networks in infrared images, this work analyzes the influence of infrared image quantization on the performance of object detection networks. Firstly, the traditional infrared quantization methods and deep learning-based object detection networks are introduced, and the characteristics of these methods are analyzed. Then, the influence of four typical quantization methods on the performances of two object detection networks is compared, and the influence mechanism is analyzed through a cross-comparison experiment. The experimental results show that infrared image quantization is more helpful for learning the discriminative feature of the object/background for the object detection networks. Moreover, the feature difference of object/background caused by different quantization methods will seriously affect the performance of object detection networks. The research provides support for the application of deep learning-based detection networks in infrared scenes, which is of great significance. Key words：Object detection; Deep learning networks; Infrared image quantization; Experimental comparative analysis PrefaceIn recent years, with the rapid development of deep learning, new target detection algorithms continue to emerge, and intelligent target detection algorithms based on deep learning networks have gradually been applied to military reconnaissance, missile guidance and other fields. Infrared images have the characteristics of large dynamic range and concentrated gray level. In the traditional target detection framework, it is often necessary to quantitatively enhance the infrared image to improve the contrast between the target and the background, thereby improving the detection performance. However, for deep learning detection networks, given their powerful feature learning and representation capabilities, whether infrared image quantification is necessary and the impact of quantification methods on detection performance remains to be studied. This paper analyzes the characteristics of different infrared image quantization methods, compares the effects of different quantization methods on the performance of the detection network, studies the feature differences learned by the detection network in different quantized images, and gives suggestions for the selection of quantization methods in practical applications.

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Section: Methods Based On Linear Quantizationmentioning

confidence: 99%

Influence analysis of wide dynamic infrared image quantization on target detection performance

Jiang¹,

Cao

Shen³

et al. 2023

Conference on Infrared, Millimeter, Terahertz Waves and Applications (IMT2022)

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“…式中 ρ 为等离子体的密度，υ 为等离子体的速度，σ 为电导率， P 为流体受到的压力， J 为电流密度， E 为动态熵，λ 为气体的热传导系数， SR 为辐射项， T 为气体的温度。由(1)式可以看出高压电弧等离子的温度与电流密度的平方有关。 4 弧区温度测量高压电弧的温度影响着耦合器的制造性能。Rego 等 [16] 采用 S 型由铂铑 10-铂热电偶测量电弧放电区域温度的大小，将热电偶放入毛细玻璃管中，测得电弧的温度为 1420 ℃±20 ℃，但玻璃管放在电弧中长时间会发生变形。Wiley 等 [17] 将多模光纤固定在手动二维平台上，光纤的一端放在电弧放电区，光纤的另一端与红外敏感光度计相连，测得电弧放电区的温度为 1660 ℃±34 ℃，但当光纤发生变形时，需要及时更换光纤。由于石英加热槽的宽度为 1 mm，长度为 11 mm，工作区间比较窄，加热区的温度比较高，且槽中存在着高能量的带电粒子，电极两端存在非常高的电势差，很难直接测量弧区的温度，因此采用非接触测量的方法测量高压电弧的弧区温度。红外热像仪可以非接触式地测量物体表面的温度，具有响应快、精度高、测量范围广、实时性强等优点，能够形象直观地显示被测物体表面的温度分布情况 [18][19] 。本文采用美国 FILR 公司的红外热像仪 SC325 测定高压电弧弧区的温度分布。 SC325 具有 0.05 ℃ 的热灵敏度，测量的最高温度为 1300 ℃。由于红外热像仪的测量波长为 750~1300 nm，而高压电弧的波长约为 255.75~409.08 nm [20] Fig.7 Temperature distribution along the line AB in Fig [22] 。经过 ANSYS 瞬态分析，光纤温度和时间的变化如图 13 所示，光纤加热区域温度逐渐升高，并且热量向非加热区域传递，经过一段时间后光纤的温度达到稳定，光纤温度和弧区温度基本相等。图 14 为光纤上 A、 B、 C 3 点的温度和加热时间的变化图，经过 25 s，光纤温度基本达到稳定。图 11 电弧加热温度稳定曲线 Fig.11 Temperature stability curves of arc heating 图 10 加热距离与弧区温度的关系图…”

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Analysis and Experiments of the Arc Heating for Fiber Fused Biconical Taper Technology

Wei¹,

Wei-bin²,

Wei³

et al. 2015

中国激光

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The heating temperature is one of the key factors in the fiber fused biconical taper manufacture, which can affect the component performance. In order to improve the temperature stability and controllability in the heating region, the high-voltage arc heating device is presented and arc heating, heating region temperature and fiber preheating are studied. The high-frequency high voltage power source, in which the current and frequency can be adjusted independently, and electrodes are designed. The closed-loop controls

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Infrared Image Dynamic Range Compression and Contrast Enhancement Based on Optimal Mapping Curve

Cited by 2 publications

References 0 publications

Influence analysis of wide dynamic infrared image quantization on target detection performance

Influence analysis of wide dynamic infrared image quantization on target detection performance

Analysis and Experiments of the Arc Heating for Fiber Fused Biconical Taper Technology

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