一种基于参数自适应引导滤波的红外图像细节增强算法

欧阳慧明; 夏丽昆; 李泽民; 何燕; 朱晓杰; 朱尤攀; 曾邦泽; 周永康

一种基于参数自适应引导滤波的红外图像细节增强算法

1.
昆明物理研究所, 云南昆明 650223
2.
陆军装备部驻重庆地区军事代表局驻昆明地区第一军事代表室, 云南昆明 650023
3.
中国船舶工业系统工程研究院, 北京海淀 100094

详细信息

作者简介:
欧阳慧明（1991-），男，白族，硕士，工程师。主要研究方向：红外成像系统设计及相关技术。E-mail: 799049681@qq.com

通讯作者:
周永康（1991-），男，硕士，工程师，红外成像系统设计及相关技术。E-mail：zyk1120102464@163.com

中图分类号: TP751.1
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- 被引次数: 0
出版历程
- 收稿日期: 2021-04-18
- 修回日期: 2021-06-23
- 刊出日期: 2022-12-20

An Infrared Image Detail Enhancement Algorithm Based on Parameter Adaptive Guided Filtering

1.
Kunming Institute of Physics, Kunming 650223, China
2.
The First Military Representative Office in Kunming of the Army Department in Chongqing, Kunming 650023, China
3.
System Engineering Research Institute of China State Shipbuilding Corporation, Beijing 100094, China

摘要

摘要: 图像分层滤波器中引导滤波器因其滤波保边效果好和计算复杂度低，在红外图像细节增强领域得到了广泛的研究与应用。但传统的引导滤波器固定的正则化参数ε不能在所有场景下都取得较好的滤波分层效果，所以本文提出基于局部方差的参数ε自适应算法，以提高引导滤波器场景适应性。此外本文进一步通过自适应参数ε值，提出了改进的基于噪声掩膜函数的细节层自适应增强算法，从而在有效抑制了图像噪声水平同时提高了算法在不同场景下的细节增强能力。
- 引导滤波 /
- 参数自适应 /
- 噪声掩膜函数 /
- 噪声抑制 /
- 细节增强
Abstract: Of all the image layered filters, guided filter has been widely studied and applied in the field of infrared image detail enhancement because of its good edge preserving effect and low computational complexity. However, traditional fixed regularization parameter ε of the guide filter cannot achieve good filtering layering effect in all scenarios. Therefore, an adaptive algorithm of parameter ε based on local variance is proposed in this paper to improve the adaptability of the guide filter in all scenarios. In addition, an improved detail layer adaptive enhancement algorithm based on noise mask function is proposed by using the adaptive parameter ε value, which can effectively suppress the noise level and improve the detail enhancement ability of the algorithm in different scenes.
- guided filtering /
- parameter adaptation /
- noise mask function /
- noise suppression /
- detail enhancement

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图 1 不同能见度场景下ε的不同取值时引导滤波细节增强处理结果

Figure 1. Guided filtering detail enhancement processing results for different values of ε in different definition scenes

下载: 全尺寸图片幻灯片

图 2 细节纹理丰富图像和细节纹理不丰富图像局部方差直方图对比

Figure 2. Local variance histogram comparison of images with rich detail texture and images without rich detail texture

下载: 全尺寸图片幻灯片

图 3 本文自适应ε引导滤波细节增强算法处理结果（k取2）

Figure 3. Processing results of the adaptive ε-guided filtering detail enhancement algorithm in this paper (k = 2)

下载: 全尺寸图片幻灯片

图 4 不同能见度下基于GF & DDE细节层处理方法和本文细节层处理方法结果对比

Figure 4. Comparison of the results of the detail layer processing method based on GF & DDE and the detail layer processing method in this paper in different definition scenes

下载: 全尺寸图片幻灯片

图 5 场景一：细节丰富的清晰室外场景

Figure 5. Scene 1: a clear outdoor scene with rich details

下载: 全尺寸图片幻灯片

图 6 场景二：细节纹理模糊的室外场景

Figure 6. Scene 2: outdoor scene with hazy details and texture

下载: 全尺寸图片幻灯片

图 7 场景三：四杠靶图像

Figure 7. Scene 3: four-bar target image

下载: 全尺寸图片幻灯片

表 1 各算法在3个场景下处理图像的AG值和EMEE值结果对比

Table 1. Comparison of AG values and EMEE values of images processed by each algorithm in 3 scenes

	Method	AGC	CLAHE	HALEQ	BF & DRP	GF & DDE	Proposed
Scene 1	AG	26.8014	59.8192	38.0070	95.156	78.1382	102.9149
Scene 1	EMEE	142.5497	264.5845	157.5203	167.1697	185.0839	249.4993
Scene 2	AG	21.9576	55.4266	29.5654	72.2097	60.2035	87.1753
Scene 2	EMEE	28.4475	138.0465	86.2441	135.8672	106.4596	174.7866
Scene 3	AG	25.2533	42.7458	42.9988	42.9236	37.0748	47.7793
Scene 3	EMEE	16.7344	43.2476	33.1936	34.8786	28.7109	45.5171

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