A High Dynamic Range Compression Technique Based on Infrared Contrast Enhancement
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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。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.
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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)
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
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 
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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
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