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《山东大学学报(理学版)》 ›› 2020, Vol. 55 ›› Issue (9): 72-80.doi: 10.6040/j.issn.1671-9352.0.2020.202

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基于变分结构引导滤波的低照度图像增强算法

林明星2,*()   

  1. 1. 山东交通职业学院机电工程学院, 山东 潍坊 261206
    2. 山东大学机械工程学院, 山东 济南 250061
  • 收稿日期:2020-05-08 出版日期:2020-09-20 发布日期:2020-09-17
  • 通讯作者: 林明星 E-mail:mxLin@sdu.edu.cn
  • 作者简介:董雪(1986—)，女，硕士，讲师,研究方向为视觉检测、图像处理. E-mail：dongxue966@126.com
  • 基金资助:
    山东省重点研发计划(重大科技创新工程)项目(2019JZZY020703);山东交通职业学院青年骨干教师国内访问学者项目

Low-light image enhancement algorithm based on variational structure

Ming-xing LIN2,*()   

  1. 1. Techanical and Electrical Engineering Department, Shandong Transport Vocational College, Weifang 261206, Shandong, China
    2. School of Mechanical Engineering, Shandong University, Jinan 250061, Shandong, China
  • Received:2020-05-08 Online:2020-09-20 Published:2020-09-17
  • Contact: Ming-xing LIN E-mail:mxLin@sdu.edu.cn

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摘要：

针对低光照条件下获取的图像存在亮度低、可见性差等缺陷，提出了基于变分结构引导滤波的低照度图像增强算法。首先在输入图像的每个像素点处计算R、G、B三通道的最大值,并使用最大值滤波获取亮通道图。其次,创新性地构建基于变分模型的引导滤波器对亮通道图进行精炼从而估计出照射分量,并根据Retinex理论,去除照射分量得到反射分量。最后,采用同态滤波和线性拉伸进一步提高反射分量的对比度以输出清晰的图像。综合实验表明,该算法能快速有效地增强低照度图像的亮度和对比度,且能较好地保持图像细节。

关键词: 低照度图像, Retinex, 变分模型, 引导滤波, 同态滤波

Abstract:

Aiming at the problem that the images captured under low illumination present some defects, such as low brightness and poor visibility, a low-light image enhancement algorithm based on variational structure guided filtering is proposed. First, the maximum of R, G and B channels are searched at each pixel of the input image, and the bright channel map is obtained by using the maximum filtering. Secondly, a novel guided filtering based on variational model is constructed to refine the bright channel image to estimate the illumination component. Subsequently, the reflection component is obtained by removing the illumination component according to the Retinex model. Finally, homomorphic filtering and linear stretching are employed to further improve the contrast of the reflection component to output an enhanced image with clear visibility. Comprehensive experimental results show that this algorithm can effectively and quickly enhance the illumination and contrast of low-light images and retain details.

Key words: low-light image, Retinex, variational model, guided filtering, homomorphic filtering

中图分类号: 

  • TP391.41

图1

Retinex模型"

图2

算法示意图"

图3

光晕效应"

图4

估计的照射分量"

图5

4种图像增强算法效果对比"

图6

4种增强算法的局部放大图"

表1

不同算法平均指标比较"

测试图像 评价指标 原图 MSRCR算法 ALSM算法 NPEA算法 本文算法
图a 对比度 509.95 513.34 466.77 616.71 531.88
信息熵 3.58 4.45 5.06 4.93 5.22
LOE — 266.40 396.55 214.57 188.79
运行时间(s) — 1.01 28.85 28.59 1.53
图b 对比度 210.25 217.46 696.55 1 057.02 440.52
信息熵 4.02 3.89 4.66 5.17 4.81
LOE — 194.76 294.01 679.54 232.50
运行时间(s) — 1.28 55.74 58.87 2.76
图c 对比度 8.79 337.74 79.97 384.38 446.81
信息熵 1.03 2.79 1.14 1.91 3.26
LOE — 354.97 256.44 301.35 305.08
运行时间(s) — 1.30 32.59 46.47 1.83
图d 对比度 45.75 1 530.11 1 300.21 1 242.26 2 267.35
信息熵 3.02 5.53 5.39 5.76 6.27
LOE — 184.42 207.60 467.38 129.01
运行时间(s) — 1.07 16.97 21.14 1.09
图e 对比度 121.87 122.05 227.60 342.48 346.26
信息熵 2.88 3.08 4.21 3.78 4.09
LOE — 333.30 256.60 283.84 287.54
运行时间(s) — 4.52 178.43 133.72 11.01
图f 对比度 166.80 327.72 439.99 334.66 441.50
信息熵 3.92 3.49 3.55 3.35 3.78
LOE — 1 796.78 389.27 703.72 291.33
运行时间(s) — 1.95 91.84 55.95 4.33
图g 对比度 385.02 427.07 2 120.26 700.07 882.83
信息熵 4.35 4.62 5.62 5.22 5.37
LOE — 1 622.83 238.81 271.75 257.89
运行时间(s) — 1.23 40.07 31.89 1.87
图h 对比度 361.12 346.66 523.40 471.64 553.95
信息熵 4.42 4.67 5.25 5.00 5.18
LOE — 168.73 364.20 345.64 192.04
运行时间(s) — 1.16 16.15 24.08 1.19
平均值 对比度 226.19 477.77 731.84 643.65 737.89
信息熵 3.40 4.07 4.36 4.39 4.75
LOE — 615.27 300.43 408.47 235.52
运行时间(s) — 1.69 57.58 50.09 3.20
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