基于双直方图均衡算法的红外图像增强

闫哲; 蒋砾; 杨帆; 罗志斌; 贾赞; 张巍; 朱洪洋; 陈如造; 朱光明; 郭小军; 刘孟然

基于双直方图均衡算法的红外图像增强

昆明物理研究所, 云南昆明 650032

详细信息

作者简介:
闫哲（1992-），男，河南省辉县市人，硕士，工程师，主要研究方向：红外图像处理。E-mail: yanzhe925@live.com

通讯作者:
蒋砾（1979-），男，云南省昆明市人，正高级工程师，主要研究方向：红外图像处理。E-mail: jiang_li@263.net.cn

中图分类号: TP391
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- 文章访问数: 194
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- PDF下载量: 49
- 被引次数: 0
出版历程
- 收稿日期: 2021-12-29
- 修回日期: 2022-02-09
- 刊出日期: 2022-09-20

Bi-Histogram Equalization Algorithm for Infrared Image Enhancement

Kunming Institute of Physics, Kunming 650223, China

摘要

摘要: 为了抑制全局直方图均衡产生的灰度饱和和局部细节丢失的情况，提出了一种双直方图均衡算法。首先对图像的背景和前景进行分割，提出基于直方图的局部最小值和修正的K-Means聚类算法来确定图像的理想分割阈值，然后再对分割的子图分别作全局直方图均衡（Global Histogram Equalization，GHE）。对该算法进行了实验验证，结果表明，相较于GHE算法，经该算法增强后的图像峰值信噪比（Peak Signal to Noise Ratio，PSNR）提高约16.425%，结构相似度（Structural Similarity Index，SSIM）提高约14.85%。同时通过主观分析，基于直方图局部最小值和修正的K-Means聚类算法的图像分割进行双直方图均衡可以有效抑制GHE算法产生的灰度饱和和细节丢失现象。
- 图像分割 /
- 双直方图均衡 /
- K-Means /
- 局部最小值
Abstract: A modified bi-histogram equalization algorithm is proposed to suppress gray saturation and loss of local details caused by global histogram equalization. First, the background and foreground of the image are segmented, and a modified k-means clustering algorithm based on the local minimum of the histogram is proposed to determine the ideal segmentation threshold of the image. Then, histogram equalization is performed for the segmented sub-graphs. The algorithm is verified by experiments; the results for the experiment show that, compared with those from global histogram equalization, the peak signal to noise ratio and structural similarity are improved by approximately 16.425% and 14.85%, respectively. Simultaneously, through subjective evaluation, the algorithm based on histogram local minimum and modified k-means can effectively suppress the gray saturation and detail loss caused by GHE.
- image segmentation /
- double histogram equalization /
- K-Means /
- local minimum

HTML全文

图 1 线性映射、灰度平移、GHE三种灰度压缩方法比较

Figure 1. Linear mapping, Gray translation and GHE comparison of three gray compression methods

下载: 全尺寸图片幻灯片

图 2 K-Means聚类效果

Figure 2. K-means clustering effect

下载: 全尺寸图片幻灯片

图 3 场景1直方图平滑前后对比

Figure 3. Histogram of Scene 1 before and after smoothing

下载: 全尺寸图片幻灯片

图 4 本文算法与GHE算法效果比较

Figure 4. The comparison between this algorithm and GHE algorithm

下载: 全尺寸图片幻灯片

表 1 本文算法增强后图像与GHE算法增强后图像的指标对比

Table 1. The index comparison between the image enhanced by this algorithm and the image enhanced by GHE algorithm

	Scene number	GHE	Algorithm in this paper
PSNR/dB	Scene1	48.5525	57.265
	Scene2	32.8	37.0987
	Scene3	28.3774	33.1055
	Scene4	33.0233	38.9685
SSIM	Scene1	0.9349	0.9515
	Scene2	0.6845	0.7820
	Scene3	0.3256	0.4260
	Scene4	0.6197	0.6988

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