| 基于改进大气散射模型的单幅图像去雾方法 |
| |
| 引用本文： | 杨勇,邱根莹,黄淑英,万伟国,胡威.基于改进大气散射模型的单幅图像去雾方法[J].北京航空航天大学学报,2022,48(8):1364-1375. |
| |
| 作者姓名： | 杨勇 邱根莹 黄淑英 万伟国 胡威 |
| |
| 作者单位： | 1.江西财经大学 信息管理学院, 南昌 330032 |
| |
| 基金项目： | 国家自然科学基金61862030国家自然科学基金62072218江西省自然科学基金20192ACB20002江西省自然科学基金20192ACBL21008 |
| |
| 摘 要： | 雾天情况下获得的图像通常会出现对比度低、色彩丢失及噪声等问题，传统的去雾方法主要着眼于解决对比度低、色彩损失等问题，而没有考虑空气中灰尘颗粒散射隐藏的噪声光，导致去雾结果中易出现大量的噪声。针对该问题，提出了一种基于改进大气散射模型的单幅图像去雾方法。结合雾霾天气的特点，通过增加空气中介质散射的噪声光对传统雾天成像的大气散射模型进行改进；针对暗通道先验计算透射率不准确的问题，根据改进的模型构建一种透射率精细化的求取方法；结合全变分模型保边抑噪的思想，构造一种新的目标函数，迭代求解获得去雾图像。实验结果和对比分析表明:所提方法能有效去除图像中的雾，减少去雾结果中的噪声，同时也能保留图像中丰富的纹理信息。
|
| 关 键 词： | 图像去雾 大气散射模型 暗通道先验 目标函数 自适应权重 |
| 收稿时间： | 2021-09-06 |
Single image dehazing method based on improved atmospheric scattering model |
| |
| Affiliation: | 1.School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330032, China2.School of Software, Tiangong University, Tianjin 300387, China3.School of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang 330032, China |
| |
| Abstract: | Images obtained in foggy conditions often suffer from low contrast, color loss, and noise. At present, many traditional dehazing methods mainly focus on solving problems such as low contrast and color loss, but do not consider the hidden noise light scattered by dust particles in the air, resulting in a large amount of noise in the dehazing results. This work provides an image dehazing algorithm based on an enhanced atmospheric scattering model to address the mentioned problems. Firstly, according to the characteristics of haze, the traditional atmospheric scattering model of hazy imaging is improved by adding the noise light reflected by the medium in the air. Then, in order to address the transmission calculation inaccuracy problem for the dark channel prior, a refined calculation method of transmission is constructed according to the improved model. Finally, combined with the idea of edge preservation and noise suppression of the total variation model, a new objective function is constructed and solved iteratively to obtain the final defogging image. A large number of experimental results and comparative analyses show that the proposed method can effectively remove the haze in the image, reduce the noise in the dehazing results, and retain the rich texture information in the image. |
| |
| Keywords: | |
|
| 点击此处可从《北京航空航天大学学报》浏览原始摘要信息 |
|
点击此处可从《北京航空航天大学学报》下载全文 |
|
