Visibility restoration for real-world hazy images via improved physical model and Gaussian total variation  

作　　者：Chuan LI Enping HU Xinyu ZHANG Hao ZHOU Hailing XIONG Yun LIU 

机构地区：[1]College of Computer and Information Science,Southwest University,Chongqing 400715,China [2]School of Big Data and Intelligent Engineering,Chongqing College of International Business and Economics,Chongqing 401520,China [3]College of Artificial Intelligence,Southwest University,Chongqing 400715,China [4]College of Electronic and Information Engineering,Southwest University,Chongqing 400715,China

出　　处：《Frontiers of Computer Science》2024年第1期267-269,共3页中国计算机科学前沿（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant No.62301453);the Natural Science Foundation of Chongqing,China(No.cstc2020jcyj-msxmX0324).

摘　　要：1 Introduction Under real-world haze conditions,the existence of haze particles in the atmosphere reduces the visibility of captured image.Furthermore,the noise is inevitably introduced into the degraded image,which further deteriorates the visual quality of the images.To enhance the visibility and quality of outdoor real-world hazy images,numerous algorithms have been proposed to remove haze from a single input image.The existing methods are broadly lumped into two categories:prior-based methods[1,2]and learning-based methods[3–6].Unfortunately,the widely used atmospheric scattering model and the corresponding haze removal methods fail to take the noise interference into account,which may result in poor visibility restoration performance.

关 键 词：VISIBILITY REMOVE noise 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]