Bionic image enhancement algorithm based on top-bottom hat transformation

doi:10.11772/j.issn.1001-9081.2019081496

Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (5): 1440-1445.DOI: 10.11772/j.issn.1001-9081.2019081496

• Virtual reality and multimedia computing • Previous Articles Next Articles

Bionic image enhancement algorithm based on top-bottom hat transformation

YU Tianhe, LI Yuzuo, LAN Chaofeng

School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin Helongjiang 150080, China

Received:2019-09-09 Revised:2019-11-13 Online:2020-05-10 Published:2020-05-15
Contact: LI Yuzuo, born in 1995, M. S. candidate. His research interests include image processing.
About author:YU Tianhe, born in 1972, Ph. D., professor. His research interests include instrument, electrical automation, image processing.LI Yuzuo, born in 1995, M. S. candidate. His research interests include image processing.LAN Chaofeng, born in 1981,Ph. D., associate professor. Her research interests include image processing.
Supported by:
This work is partially supported by the National Natural Science Foundation of China (11804068).

基于顶帽底帽变换的仿生图像增强算法

于天河, 李昱祚, 兰朝凤

哈尔滨理工大学电气与电子工程学院，哈尔滨 150080

通讯作者: 李昱祚(1995—)
作者简介:于天河(1972—)，男，黑龙江哈尔滨人，教授，博士，主要研究方向：仪器、电气自动化、图像处理；李昱祚(1995—)，男，河南驻马店人，硕士研究生，主要研究方向：图像处理；兰朝凤（1981—），女，黑龙江绥化人，副教授，博士，主要研究方向：图像处理。
基金资助:
国家自然科学基金资助项目(11804068)。

Abstract

Abstract:

In view of the low contrast, poor details and low color saturation of low-illumination images, by analyzing the non-linear relationship between the subjective brightness sensation of the human eye and the transmission characteristics of the receptive field in the retinal ganglion cells of the human eye, a bionic image enhancement algorithm combining top-hat transformation and bottom-hat transformation was proposed. Firstly, the RGB (Red, Green, Blue) color space of low-illumination image was converted into HSV (Hue, Saturation, Value) space, and the global brightness logarithmic transformation was performed on the brightness component. Secondly, the retinal neuron receptive wild tri-Gaussian model was used to adjust the contrast of the local edge of the image. Finally, top-hat transformation and bottom-hat transformation were used to assist the extraction of background with high brightness. The experimental results show that the low-illumination images enhanced by the proposed algorithm have clear details and high contrast, without the problems of uneven illumination and image depth of field in the images captured by the device. These enhanced images have high color saturation and strong visual sensation effect.

Key words: image enhancement, contrast, details, top-hat transformation, bottom-hat transformation, tri-Gaussian model

摘要：

针对低照度图像对比度低、细节模糊、色彩饱和度低的问题，通过分析人眼的主观亮度感受和光照强度的非线性关系以及人眼的视网膜神经节细胞中感受野的传输特性，提出一种顶帽变换和底帽变换相结合的仿生图像增强算法。首先，将低照度图像的RGB色彩空间转换为HSV空间，对亮度分量进行全局亮度对数变换；其次，采用视网膜神经元感受野三高斯模型对图像局部边缘的对比度进行调整；最后，用顶帽变换和底帽变换辅助对较亮背景的提取。实验结果表明，所提算法增强的低照度图像不仅细节清楚、对比度高，同时还没有设备采集图像存在的光照不均匀和图像景深的问题，而且增强后的图像色彩饱和度高，具有很强的视觉感受效果。

关键词: 图像增强, 对比度, 细节, 顶帽变换, 底帽变换, 三高斯模型

CLC Number:

TP391

YU Tianhe, LI Yuzuo, LAN Chaofeng. Bionic image enhancement algorithm based on top-bottom hat transformation[J]. Journal of Computer Applications, 2020, 40(5): 1440-1445.

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.

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Bionic image enhancement algorithm based on top-bottom hat transformation

基于顶帽底帽变换的仿生图像增强算法

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