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摘要: 图像增强算法能够提高图像整体和局部的对比度，突出图像的细节信息，使增强后的图像更符合人眼的视觉特性且易于机器识别，在军事和民用领域具有广泛的应用。本文从图像增强算法的原理出发，归纳总结了近年来应用比较广泛的4类图像增强算法及其改进算法，包括直方图均衡图像增强算法、小波变换图像增强算法、偏微分方程图像增强算法和基于Retinex理论的图像增强算法。结合人眼视觉特性、噪声抑制、亮度保持和信息熵最大化等图像增强的改进算法，在保证增强图像具有较高对比度的前提下，可进一步提升图像的质量。实现了9种较为典型的图像增强算法，采用主观和客观的评价方法对增强效果进行了对比，分析了不同增强算法的优缺点，并给出了这些算法的计算时间。对这些算法的深入研究能够推动图像增强技术向更高水平发展，从而使图像增强技术在多个学科领域发挥重要作用。Abstract: Image enhancement algorithms can enhance contrast between the whole and partial images, and highlight the details of images. It also can make the enhanced images more in line with the visual characteristics of the human eyes and it applies to machine identification, which has a wide range of applications in military and civilian fields. Based on the principle of image enhancement algorithm, four types of image enhancement algorithms and their improved algorithms are summarized in this paper. These algorithms include histogram equalization image enhancement algorithm, wavelet transform image enhancement algorithm, partial differential equation image enhancement algorithm and Retinex image enhancement algotithm. These improved algorithms, which combine the human visual characteristics, noise suppression, brightness preserving and information entropy maximization, can further improve the quality of images in addition to enhancing the contrast. In this paper, nine typical image enhancement algorithms are implemented, and their enhancement effects are compared with subjective and objective evaluation methods. The advantages and disadvantages of these enhancement algorithms are analyzed, and the calculation time of the algorithms are given. The study on these algorithms can promote the image enhancement technology to a higher level, so as to make the image enhancement technology play an important role in many fields.
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图 1 不同算法得到的增强图像及其对应的直方图(1)
Figure 1. Enhanced images obtained by different algorithms and their corresponding histograms (1)
图 2 不同算法得到的增强图像及其直方图(2)
Figure 2. Enhanced images obtained by different algorithms and their corresponding histograms (2)
表 1 不同算法得到的图像质量的客观评价结果
Table 1. Objective evaluation results of images quality obtained by different algorithms
Evaluation
resultOriginal HE BBHE LMHE WT KGWT PDE TVPDE SSR MSR 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 Contrast 8.7 18.3 31.7 24.9 25.3 26.8 28.5 30.5 29.0 33.9 24.9 36.1 33.5 42.3 38.1 28.4 23.7 38.0 27.4 30.6 Signal to
noise ratio7.5 9.4 10.3 12.5 9.7 14.1 13.9 13.6 11.4 10.3 12.4 13.9 23.0 13.7 28.3 16.3 30.6 21.5 32.9 24.9 Information
entropy1.6 3.0 3.7 3.4 3.4 3.7 3.9 4.0 3.8 4.3 3.6 4.6 4.2 4.7 4.1 4.5 4.8 5.1 5.1 5.0 
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表 2 不同算法的计算时间(ms)
Table 2. Computation time of different algorithms (ms)
Resolution/(pixel×pixel) HE BBHE LMHE WT KGWT PDE TVPDE SSR MSR 256×256 3 5 7 18 22 17 28 13 30 640×512 11 15 34 95 108 90 116 66 125 1024×1024 36 45 109 306 381 277 316 208 343
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