Infrared Image Enhancement Based on Regional Adaptive Multiscale Intense Light Fusion
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摘要: 图像增强可以分为全局增强和局部增强两种技术，当前基于局部的图像增强技术无法准确地对目标和背景进行分割且难以自适应地对分割区域进行增强。本文提出了一种区域自适应多尺度强光融合算法用于红外图像的增强处理。该算法首先使用语义分割技术完成目标区域和背景区域的划分，然后使用改进后的多尺度强光融合算法分别对各区域进行自适应增强。实验结果表明，所提算法的增强效果均优于当前主流算法，图像增强的视觉效果更真实。Abstract: Image enhancement can be divided into two kinds: global enhancement and local enhancement. Current image enhancement techniques based on local enhancement cannot accurately segment the target area and background, and it is difficult to enhance the segmentation region adaptively. In this paper, a region-adaptive multi-scale strong light fusion algorithm is proposed for infrared image enhancement. Firstly, semantic segmentation technology is used to divide the target area and background area. Then, the improved multi-scale strong light fusion algorithm is used to enhance each area adaptively. The experimental results show that the enhancement effect of the proposed algorithm is better than that of the current conventional algorithms, and the visual effect of image enhancement is more realistic.
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Keywords:
- infrared images /
- image enhancement /
- intense light fusion /
- multi scale
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图 1 区域自适应多尺度强光融合算法流程图
Figure 1. The flow chart of the regional adaptive multiscale intense light fusion algorithm
表 1 算法有效性验证的评价指标对比
Table 1 Comparison of evaluation indexes of algorithm validity verification
Brenner Entropy Input image 0.4907 6.3527 Single scale 0.9100 6.5170 Multiscale 1.0244 6.5541 Proposed 1.4699 6.6882 
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表 2 不同算法增强后的评价指标对比
Table 2 Comparison of evaluation indexes of different algorithms
Input image LIME NPE CRM MF BIMEF MSRCR Proposed Brenner 0.8259 1.5939 1.3525 0.9502 1.0140 0.8831 0.4191 2.2327 Entropy 6.1967 6.7101 6.6354 6.4660 6.5497 6.5351 6.1148 6.7776
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