一种基于红外对比度提升的高动态范围压缩技术

公志强; 刘仁军; 汪利庆; 彭玲; 罗音

一种基于红外对比度提升的高动态范围压缩技术

武汉华中数控股份有限公司，湖北武汉 430000

基金项目:

武汉市企业技术创新专项 2020010602011989

详细信息

作者简介:
公志强（1985-），男，汉族，江西南昌人，硕士，工程师，主要研究方向：红外图像处理。E-mail：gongzq@hzncc.com

通讯作者:
刘仁军（1992-），男，汉族，湖北仙桃人，硕士，工程师，主要研究方向：图像处理。E-mail：feelingjunrenliu@163.com

中图分类号: TP391
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- 被引次数: 0
出版历程
- 收稿日期: 2020-07-16
- 修回日期: 2020-10-20
- 刊出日期: 2021-08-20

A High Dynamic Range Compression Technique Based on Infrared Contrast Enhancement

Wuhan Huazhong Numerical Control Co. Ltd., Wuhan 430000, China

摘要

摘要: 如何将红外探测器采集的高动态范围的数据压缩为低动态范围图像数据的同时，能尽可能地保留图像的信息，提高图像的对比度一直是一个技术难点。针对这一问题，本文提出了一种新的红外图像压缩方法。该方法引入了直方图信息，通过对直方图进行分割，区分背景区域像素和目标区域像素; 然后计算压缩映射模型; 最后结合分割后的直方图对图像的像素采用不同强度的对比度增强。本文算法利用直方图区分背景区域像素与目标区域像素，在增强图像对比度时，能有效抑制背景噪声。通过实验对比，结果表明，本文所提出的算法更能较好地突出图像的细节，增强图像对比度。
- 高动态范围 /
- 红外图像压缩 /
- 对比度增强 /
- 直方图分割
Abstract: It has always been technically difficult to compress the high dynamic range data collected by an infrared detector to low dynamic range image data, while preserving the image information as much as possible and improving the contrast of the image. To solve this problem, a new infrared image compression method was proposed. In this method, histogram information is introduced, and the pixels of the background and target regions are distinguished by the segmentation of the histogram. Then, the compression model is established. Finally, enhancing the contrast of the image pixels using different coefficients combines the segmented histogram. The algorithm proposed in this paper uses histogram information to distinguish the pixels of the background region and the pixels of the target region and can effectively suppress background noise when enhancing the image contrast. The experimental results show that the proposed algorithm can better highlight details and improve the contrast.
- high dynamic range /
- infrared image compression /
- contrast enhancement /
- histogram segmentation

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图 1 分割点示意图（左）和最终分割点示意图（右）

Figure 1. Schematic diagram of Segmentation points (left) and schematic diagram of final segmentation points (right)

下载: 全尺寸图片幻灯片

图 2 示例图及不同滤波尺寸下的局部对比度直方图

Figure 2. Sample image and local contrast under different filter sizes

下载: 全尺寸图片幻灯片

图 3 不同算法的处理结果

Figure 3. The processing results of different algorithms

下载: 全尺寸图片幻灯片

表 1 不同算法在相同场景图像中的信息熵的对比结果

Table 1. Information entropy comparison results that different algorithms in the same scene image

Algorithm	Scene 1	Scene 2	Scene 3	Scene 4	Scene 5
Linear mapping	7.1260	6.2631	7.1262	7.4640	7.2761
Literature[6]'s algorithm	7.2788	6.7646	7.0064	7.4837	7.4046
Literature[7]'s algorithm	7.6616	6.1768	7.3264	7.6890	7.4696
Our algorithm	7.8303	6.7306	7.6266	7.7293	7.7107

下载: 导出CSV

表 2 不同算法在相同场景图像中的峰值信噪比的对比结果

Table 2. PSNR comparison results that different algorithms in the same scene image

Algorithm	Scene 1	Scene 2	Scene 3	Scene 4	Scene 5
Literature[6]'s algorithm	19.7842	24.5278	23.7493	19.2759	21.3895
Literature[7]'s algorithm	18.2785	18.7230	22.8438	20.1421	23.9762
Our algorithm	22.2963	23.3482	23.7893	21.5421	24.4664

下载: 导出CSV

参考文献(12)

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