Implementation of a Histogram Equalization Algorithm Based on Image Segmentation
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摘要: 红外原始图像对比度低，动态范围高，而且不同场景信号动态范围不一样，为了得到更好的图像效果，需要对红外图像原始数据进行灰度变换。本文针对全局直方图均衡增强算法的不足，提出了一种基于区域分割的直方图均衡增强方法。通过将图像分割成多个区域，每个区域分别做直方图均衡，然后通过线性插值对图像做像素重构，有效地改善了红外热成像系统的图像质量，对比度和图像细节信息得到很大的增强。该算法易于在FPGA中实现，且取得了较好的效果。Abstract: Original infrared images have a low contrast and high dynamic range, with the dynamic range varying between scenes. To obtain better images, it is necessary to transform the original infrared image data. Aiming to overcome the deficiency of the global histogram equalization algorithm, this study proposes a histogram equalization method based on region segmentation. By dividing the image into several regions, each region is histogram balanced; subsequently, pixels are reconstructed by linear interpolation. This effectively improves the image quality of the infrared thermal imaging system, greatly enhancing the contrast and image detail information. The algorithm is simple to implement in an FPGA and positive results are obtained.
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Key words:
- infrared image /
- histogram equalization /
- gray scale transformation /
- FPGA
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图 6 复杂场景图像对比
Figure 6. Complex scene image comparison
(a) Original image (b) Effect of Global histogram (c) Effect of this algorithm
表 1 图像信息熵
Table 1. Image information entropy
Evaluating indicator Global histogram This algorithm Information entropy of scene 1 7.51 7.55 Information entropy of scene 2 6.53 7.57 Information entropy of scene 3 7.33 7.83 
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表 2 图像标准差
Table 2. Image standard deviation
Evaluating indicator Global histogram This algorithm Standard deviation of scene 1 60.1 66.8 Standard deviation of scene 2 28.7 53.9 Standard deviation of scene 3 43.3 59.1
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