基于多尺度梯度域引导滤波的低照度图像增强算法

doi:10.11772/j.issn.1001-9081.2019040642

计算机应用 ›› 2019, Vol. 39 ›› Issue (10): 3046-3052.DOI: 10.11772/j.issn.1001-9081.2019040642

• 虚拟现实与多媒体计算 • 上一篇下一篇

基于多尺度梯度域引导滤波的低照度图像增强算法

李红, 王瑞尧, 耿则勋, 胡海峰

平顶山学院信息工程学院, 河南平顶山 467000

收稿日期:2019-04-16 修回日期:2019-06-23 出版日期:2019-10-10 发布日期:2019-08-21
通讯作者: 李红
作者简介:李红(1981-),女,重庆人,讲师,硕士,主要研究方向:图像处理、嵌入式系统;王瑞尧(1989-),男,河南平顶山人,助教,硕士,主要研究方向:图像处理、嵌入式系统;耿则勋(1958-),男,河南孟州人,教授,博士生导师,博士,主要研究方向:图像处理、计算机视觉;胡海峰(1982-),男,河南平顶山人,副教授,硕士,主要研究方向:传感器网络。
基金资助:
平顶山市科技攻关项目（201700812）；平顶山学院青年基金资助项目（PXY-QNJJ-2019010）。

Low-illumination image enhancement algorithm based on multi-scale gradient domain guided filtering

LI Hong, WANG Ruiyao, GENG Zexun, HU Haifeng

School of Information Engineering, Pingdingshan University, Pingdingshan Henan 467000, China

Received:2019-04-16 Revised:2019-06-23 Online:2019-10-10 Published:2019-08-21
Supported by:
This work is partially supported by the Pingdingshan Science and Technology Project (201700812), the Youth Fund of Pingdingshan University (PXY-QNJJ-2019010).

摘要/Abstract

摘要： 针对低照度彩色图像整体亮度较低，增强图像中颜色易失真，部分图像细节淹没在较低灰度值像素中等问题，提出一种改进的低照度图像增强算法。首先，把待处理图像转换到色调、饱和度、亮度（HSI）颜色空间，对亮度分量进行非线性全局亮度校正；然后，提出多尺度梯度域引导滤波的亮度增强模型，利用该模型对校正后的亮度分量进行增强，接着对增强后的亮度分量进一步实施避免颜色失真的亮度校正；最后，将图像再转换回红绿蓝（RGB）颜色空间。实验结果表明，增强后的图像亮度平均提高90.0%以上，清晰度平均提高123.8%以上，这主要得益于多尺度梯度域引导滤波具有更好的亮度平滑和增强能力；同时由于减小了颜色失真，使增强图像的细节表现能力平均提高18.2%以上；由于采用了多尺度梯度域引导滤波的亮度增强模型与直方图自适应的亮度校正算法，使提出的低照度图像增强算法适宜应用于夜间等弱光源条件下的彩色图像增强。

关键词: 低照度图像, 图像增强, 梯度域引导滤波, Retinex理论, HSI颜色空间

Abstract: An improved low-illumination image enhancement algorithm was proposed to solve the problems that the overall intensity of low-illumination color image is low, the color in the enhanced image is easy to be distorted, and some enhanced image details are drowned in the pixels with low gray value. Firstly, an image to be processed was converted to the Hue Saturation Intensity (HSI) color space, and the nonlinear global intensity correction was carried out for the intensity component. Then, an intensity enhancement model based on multi-scale guided gradient domain filtering was put forward to enhance the corrected intensity component, and the intensity correction was further performed to avoid color distortion. Finally, the image was converted back into Red Green Blue (RGB) color space. Experimental results show that the enhanced images have the intensity increased by more than 90.0% on average, and the sharpness increased by more than 123.8% on average, which are mainly due to the better intensity smoothing and enhancement ability of multi-scale gradient domain guided filtering. At the same time, due to the reduction of color distortion, the detail performance of enhanced images increases by more than 18.2% on average. The proposed low-illumination image enhancement algorithm is suitable for enhancing color images under night and other weak light source conditions, because of using intensity enhancement model based on multi-scale gradient domain guided filtering and histogram adaptive intensity correction algorithm.

Key words: low-illumination image, image enhancement, gradient domain guided filtering, Retinex theory, HSI color space

中图分类号:

TP391.4

李红, 王瑞尧, 耿则勋, 胡海峰. 基于多尺度梯度域引导滤波的低照度图像增强算法[J]. 计算机应用, 2019, 39(10): 3046-3052.

LI Hong, WANG Ruiyao, GENG Zexun, HU Haifeng. Low-illumination image enhancement algorithm based on multi-scale gradient domain guided filtering[J]. Journal of Computer Applications, 2019, 39(10): 3046-3052.

参考文献

[1] 毛东月, 谢正祥, 贺向前, 等. 自适应双向保带宽对数变换及低照度图像增强[J]. 中国图象图形学报, 2017, 22(10):1356-1363. (MAO D Y, XIE Z X, HE X Q, et al. Adaptive bilateral logarithm transformation with bandwidth preserving and low-illumination image enhancement[J]. Journal of Image and Graphics, 2017, 22(10):1356-1363.)
[2] SMITHA M L, SHEKAR B H. Illumination invariant text recognition system based on contrast limit adaptive histogram equalization in videos/images[C]//Proceedings of the 3rd International Symposium on Women in Computing and Informatics. New York:ACM, 2015:174-179.
[3] 姜柏军,钟明霞. 改进的直方图均衡化算法在图像增强中的应用[J]. 激光与红外, 2014, 44(6):702-706. (JIANG B J, ZHONG M X. Improved histogram equalization algorithm in the image enhancement[J]. Laser & Infrared, 2014, 44(6):702-706.)
[4] SINGH K, KAPOOR R. Image enhancement using exposure based sub image histogram equalization[J]. Pattern Recognition Letters, 2014, 36(1):10-14.
[5] 胡钰,李甜甜, 黄梁松,等.基于双边伽马校正的保亮度图像增强方法[J]. 计算机应用与软件, 2019, 36(5):204-210, 241. (HU Y, LI T T, HUANG L S, et al. Brightness preserving image enhancement method based on bilateral Gamma correction[J]. Computer Applications and Software, 2019, 36(5):204-210, 241.)
[6] JIANG X, YAO H, ZHANG S, et al. Night video enhancement using improved dark channel prior[C]//Proceedings of the 201320th IEEE International Conference on Image Processing. Piscataway:IEEE, 2013:553-557.
[7] 赵宏宇, 肖创柏, 禹晶. 基于双MRF模型的夜间图像增强方法[J]. 中南大学学报(自然科学版), 2016, 47(10):3361-3368. (ZHAO H Y, XIAO C B, YU J. Night color image enhancement based on dual MRF model[J]. Journal of Central South University (Science and Technology), 2016, 47(10):3361-3368.)
[8] 余春艳,徐小丹,林晖翔,等.应用雾天退化模型的低照度图像增强[J]. 中国图象图形学报, 2017, 22(9):1194-1205. (YU C Y, XU X D, LIN H X, et al. Low-illumination image enhancement method based on a fog-degraded model[J]. Journal of Image and Graphics, 2017, 22(9):1194-1205.)
[9] 遆晓光, 曲悠杨. 一种改进的色彩保持低照度图像增强方法[J]. 哈尔滨工业大学学报, 2014, 46(3):1-7. (DI X G, QU Y Y. An improved method of image enhancement with color preservation and low illumination[J]. Journal of Harbin Institute of Technology, 2014, 46(3):1-7.)
[10] PETRO A B, SBERT C, MOREL J-M. Multi-scale retinex[J]. Image Processing on Line, 2014(4):71-88.
[11] 温海滨, 毕笃彦, 马时平, 等. 消除光晕和细节增强的多尺度Retinex红外图像增强[J]. 红外技术, 2016, 38(2):149-156. (WEN H B, BI D Y, MA S P, et al. Halo-free and detail enhancement based on multi-scale Retinex for infrared image[J]. Infrared Technology, 2016, 38(2):149-156.)
[12] LI Z, ZHENG J, ZHU Z, et al. Weighted guided image filtering[J]. IEEE Transactions on Image Processing, 2015, 24(1):120-129.
[13] KOU F, CHEN W, WEN C, et al. Gradient domain guided image filtering[J]. IEEE Transactions on Image Processing, 2015, 24(11):4528-4539.
[14] 张菲菲, 谢伟, 石强,等. 人眼视觉感知驱动的梯度域低照度图像对比度增强[J]. 计算机辅助设计与图形学学报, 2014, 26(11):1981-1988. (ZHANG F F, XIE W, SHI Q, et al. A perception-inspired contrast enhancement method for low-light images in gradient domain[J]. Journal of Computer Aided Design and Graphics, 2014, 26(11):1981-1988.)
[15] 宋瑞霞, 李达, 王小春. 基于HSI色彩空间的低照度图像增强算法[J]. 图学学报, 2017, 38(2):217-223. (SONG R X, LI D, WANG X C. Low illumination image enhancement algorithm based on HSI color space[J]. Journal of Graphics, 2017, 38(2):217-223.)
[16] SUN X, LIU H, WU S, et al. Low-light image enhancement based on guided image filtering in gradient domain[J]. International Journal of Digital Multimedia Broadcasting, 2017(2):9029315.
[17] 张翔, 王伟, 肖迪. 一种改进的具有色彩保护的多尺度Retinex图像增强算法[J]. 计算机科学, 2018, 45(10):247-249. (ZHANG X, WANG W, XIAO D. Improved image enhancement algorithm based on multi-scale Retinex with chromaticity preservation[J]. Computer Science, 2018, 45(10):247-249.)
[18] HE K, SUN J, TANG X. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6):1397-1409.

基于多尺度梯度域引导滤波的低照度图像增强算法

Low-illumination image enhancement algorithm based on multi-scale gradient domain guided filtering

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	牛康力, 谌雨章, 沈君凤, 曾张帆, 潘永才, 王绎冲. 基于深度学习的双通道夜视图像复原方法[J]. 计算机应用, 2021, 41(6): 1775-1784.
[2]	贺杰安, 吴晓红, 何小海, 胡建蓉, 卿粼波. 结合图像增强和卷积神经网络的小麦不完善粒识别[J]. 计算机应用, 2021, 41(3): 911-916.
[3]	杨佳义, 陈勇. 低照度视频图像自适应补偿增强的设计与实现[J]. 计算机应用, 2020, 40(8): 2372-2377.
[4]	于天河, 李昱祚, 兰朝凤. 基于顶帽底帽变换的仿生图像增强算法[J]. 计算机应用, 2020, 40(5): 1440-1445.
[5]	王瑞尧, 岳雪亭, 周志青, 耿则勋. 最大差值图决策的低照度图像自适应增强算法[J]. 计算机应用, 2020, 40(4): 1164-1170.
[6]	司马紫菱, 胡峰. 基于模拟多曝光融合的低照度图像增强方法[J]. 计算机应用, 2019, 39(6): 1804-1809.
[7]	程宇, 邓德祥, 颜佳, 范赐恩. 基于卷积神经网络的弱光照图像增强算法[J]. 计算机应用, 2019, 39(4): 1162-1169.
[8]	娄梦莹, 袁丽莎, 刘娅琴, 万雪梅, 杨丰. 基于自适应融合的手掌静脉增强方法[J]. 计算机应用, 2019, 39(4): 1176-1182.
[9]	周冲, 刘欢, 赵爱玲, 张鹏程, 刘祎, 桂志国. 基于梯度场的工业X射线图像增强算法[J]. 计算机应用, 2019, 39(10): 3088-3092.
[10]	雷思佳, 赵凤群. 基于自适应Riesz分数阶微分的雾天图像增强[J]. 计算机应用, 2018, 38(5): 1427-1431.
[11]	杨燕, 王帆, 白海平. 基于相对透射率估计的快速图像去雾算法[J]. 计算机应用, 2016, 36(3): 806-810.
[12]	吕笃良, 贾振红, 杨杰, Nikola KASABOV. 基于非下采样剪切波变换与引导滤波结合的遥感图像增强[J]. 计算机应用, 2016, 36(10): 2880-2884.
[13]	王小元, 张红英, 吴亚东, 刘言. 基于物理模型的低照度图像增强算法[J]. 计算机应用, 2015, 35(8): 2301-2304.
[14]	蒲恬李英花程建郑虎. 基于改进交叉视觉皮质模型的彩色图像增强[J]. 计算机应用, 2012, 32(11): 3153-3156.
[15]	郭相凤贾建芳杨瑞峰葛中峰. 基于水下图像光学成像模型的清晰化算法[J]. 计算机应用, 2012, 32(10): 2836-2839.