多尺度自校正双直方图均衡化红外图像增强

张桓; 陈志盛

多尺度自校正双直方图均衡化红外图像增强

张桓^1,,
陈志盛^2, ,

1.
长沙理工大学设计艺术学院, 湖南长沙 410114
2.
长沙理工大学电气与信息工程学院, 湖南长沙 410114

基金项目:

湖南省哲学社会科学基金 19YBA020

长沙理工大学青年教师成长计划 2019QJCZ079

详细信息

作者简介:
张桓（1976-），女，硕士，讲师，主要从事数字媒体艺术设计、图像处理方面的研究。E-mail: zhanghuan@csust.edu.cn

通讯作者:
陈志盛（1975-），男，博士，副教授，硕士生导师，主要从事人工智能、机器视觉方面的研究。E-mail: chenzhisheng@csust.edu.cn

中图分类号: TP391
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出版历程
- 收稿日期: 2023-02-05
- 修回日期: 2023-03-30
- 刊出日期: 2023-11-19

Multi-scale Auto-Corrected Bi-Histogram Equalization for Infrared Image Enhancement

ZHANG Huan^1,,
CHEN Zhisheng^2, ,

1.
School of Design Art, Changsha University of Science & Technology, Changsha 410114, China
2.
School of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha 410114, China

摘要

摘要: 针对红外图像增强过程中容易饱和、细节丢失等问题，提出一种参数自设定的双直方图均衡化方法。根据灰度级累积概率密度黄金比例值将原始图像划分为两个独立的子图像。结合原始图像曝光度和子图像灰度级区间信息，对每个子图像的直方图进行多尺度自适应加权校正。基于校正后的直方图，对每个子图像分别作均衡化映射变换，最后合并子图像获得增强图像。在红外图像公开数据集INFRARED100上进行的测试显示，与亮度保持双直方图均衡化（Brightness Preserving Bi-Histogram Equalization，BBHE）、带平台限制的双直方图均衡化（Bi-histogram Equalization with a Plateau Limit，BHEPL）、基于曝光度的双直方图均衡化（Exposure based Sub-image Histogram Equalization，ESIHE）方法相比，所提方法增强的图像具有合适的平均对比度和更大的平均信息熵，在峰值信噪比（Peak Signal-to-Noise Ratio，PSNR）、结构相似度（Structural Similarity，SSIM）、绝对平均亮度偏差（Absolute Mean Brightness Error，AMBE）指标上平均提升至少17.2%、4.0%、56.2%。实验结果表明，所提方法对不同亮度特征的红外图像都有良好的适应性，可有效增强红外图像对象和背景之间的对比度，在噪声抑制、亮度和细节保持等方面优于同类方法。
- 红外图像处理 /
- 对比度增强 /
- 直方图均衡化 /
- 亮度保持 /
- 自适应加权校正
Abstract: We proposed a parameter self-tuning bi-histogram equalization method to solve saturation and detail loss in infrared image enhancement. We decomposed an input image into two independent sub-images according to the golden ratio of the gray cumulative probability density and modified each sub-image histogram through a multi-scale adaptive weighing process with input image exposure and sub-image gray-level interval information. Subsequently, we performed the equalization of the two corrected sub-histograms independently and combined the two equalized sub-images into a single output image. A test on 100 infrared images in a public dataset-INFRARED100 showed that, compared with brightness preserving bi-histogram equalization (BBHE), bi-histogram equalization with a plateau limit (BHEPL), and exposure-based sub-image histogram equalization (ESIHE), the images enhanced by the proposed method have appropriate contrast and greater average information entropy. We increased the peak signal-to-noise ratio (PSNR), structural similarity (SSIM) index, and absolute mean brightness error (AMBE) by at least 17.2%, 4.0%, and 56.2% on average. The experiments illustrated that the proposed method is adaptable to infrared images with different brightness characteristics, effectively improving the contrast between the infrared image object and background. This method is superior to noise suppression, brightness, and detail preservation methods.
- infrared image processing /
- contrast enhancement /
- histogram equalization /
- brightness preserving /
- adaptive weighting corrected

HTML全文

图 1 MABHE方法流程图

Figure 1. Flowchart of the proposed MABHE method

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图 2 红外图像直方图校正结果对比

Figure 2. Comparison of infrared image histogram correction results

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图 3 低亮度场景1增强效果对比

Figure 3. Comparison of enhancement for low brightness scene 1

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图 4 中等亮度场景2增强效果对比

Figure 4. Comparison of enhancement for medium brightness scene 2

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图 5 高亮度场景3增强效果对比

Figure 5. Comparison of enhancement for high brightness scene 3

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图 6 测试结果统计箱线图

Figure 6. Box plots of test result statistics

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表 1 基于INFARED100数据集的图像质量评价指标均值

Table 1 Average performance metric for the 100 images of the public dataset-INFARED100.

Methods	PSNR	SSIM	AMBE	IE	MG
Original	-	-	-	6.2731	4.1493
HE	11.8365	0.5221	42.6232	5.5617	14.1961
CLAHE	20.2560	0.7433	14.6675	7.1956	10.6660
BBHE[8]	14.9613	0.6359	16.4776	6.1134	11.9938
BHEPL[15]	20.1940	0.8202	7.7970	6.2118	8.2653
AGCWD[6]	13.2681	0.7777	48.8978	6.2036	8.4196
ESIHE[19]	21.2272	0.8523	16.7685	6.2119	6.9825
TSIHE[9]	22.8248	0.8771	4.1942	6.2133	7.1630
Proposed	24.8834	0.8866	3.4119	6.2530	7.0959

下载: 导出CSV

表 2 分场景图PSNR指标

Table 2 Evaluation results of PSNR metric

Methods	Scene 1	Scene 2	Scene 3
HE	10.2957	13.1403	11.8123
CLAHE	19.8973	18.5916	21.3235
BBHE[8]	14.8541	13.7137	17.5704
BHEPL[15]	18.3898	17.7603	19.8411
AGCWD[6]	13.2483	12.6241	15.1278
ESIHE[19]	22.1206	18.3078	21.2025
TSIHE[9]	19.7175	21.4374	24.3171
Proposed	20.3399	20.9097	24.8429

下载: 导出CSV

表 3 分场景图SSIM指标

Table 3 Evaluation results of SSIM metric

Methods	Scene 1	Scene 2	Scene 3
HE	0.4134	0.5559	0.4293
CLAHE	0.7408	0.7030	0.8001
BBHE[8]	0.5905	0.5599	0.7623
BHEPL[15]	0.7589	0.7378	0.8991
AGCWD[6]	0.7174	0.7962	0.9492
ESIHE[19]	0.8432	0.8317	0.8563
TSIHE[9]	0.8302	0.8175	0.8970
Proposed	0.8253	0.8319	0.8938

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表 4 分场景图AMBE指标

Table 4 Evaluation results of AMBE metric

Methods	Scene 1	Scene 2	Scene 3
HE	53.8064	21.7324	35.9310
CLAHE	15.8266	11.5483	7.7629
BBHE[8]	4.3586	12.3094	25.2210
BHEPL[15]	4.1526	2.1580	18.9198
AGCWD[6]	47.2736	54.0382	42.5067
ESIHE[19]	10.2090	26.0609	16.0833
TSIHE[9]	4.3061	0.6500	0.7722
Proposed	0.8997	4.0071	2.3998

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表 5 分场景图IE指标

Table 5 Evaluation results of IE metric

Methods	Scene 1	Scene 2	Scene 3
Original	6.0232	6.5258	5.8833
HE	5.3917	5.9028	5.2211
CLAHE	7.0593	7.5560	7.0979
BBHE[8]	5.9508	6.3433	5.7647
BHEPL[15]	6.0018	6.4437	5.8325
AGCWD[6]	5.9925	6.4525	5.8209
ESIHE[19]	5.9883	6.4571	5.8062
TSIHE[9]	6.0114	6.4242	5.8391
Proposed	6.0213	6.4925	5.8665

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表 6 分场景图MG指标

Table 6 Evaluation results of MG metric

Methods	Scene 1	Scene 2	Scene 3
Original	3.0842	3.7568	2.5470
HE	15.7834	11.7503	10.9348
CLAHE	9.3561	10.3328	6.1693
BBHE[8]	9.9451	11.7182	5.8128
BHEPL[15]	7.5330	8.8694	4.6992
AGCWD[6]	8.4280	7.3790	3.7168
ESIHE[19]	6.0012	6.1858	4.5910
TSIHE[9]	6.8817	7.5362	4.2996
Proposed	6.9340	7.3872	4.7513

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