AIDEDNet:anti-interference and detail enhancement dehazing network for real-world scenes

作     者：Jian ZHANG Fazhi HE Yansong DUAN Shizhen YANG Jian ZHANG;Fazhi HE;Yansong DUAN;Shizhen YANG

作者机构：School of Computer ScienceWuhan UniversityWuhan 430072China College of Sport Engineering and Information TechnologyWuhan Sports UniversityWuhan 430079China School of Remote Sensing and Information EngineeringWuhan UniversityWuhan 430072China 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2023年第17卷第2期

页      面：225-235页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by the National Natural Science Foundation of China(Grant No.62072348) the National Key RD Program of China under(2019YFC1509604) the Science and Technology Major Project of Hubei Province China(Next-Generation AI Technologies)(2019AEA170) 

主　　题：dehaze anti-interference detail enhancement network 

摘      要：The haze phenomenon seriously interferes the image acquisition and reduces image *** to many uncertain factors,dehazing is typically a challenge in image *** most existing deep learning-based dehazing approaches apply the atmospheric scattering model(ASM)or a similar physical model,which originally comes from traditional dehazing ***,the data set trained in deep learning does not match well this model for three ***,the atmospheric illumination in ASM is obtained from prior experience,which is not accurate for dehazing ***,it is difficult to get the depth of outdoor scenes for ***,the haze is a complex natural phenomenon,and it is difficult to find an accurate physical model and related parameters to describe this *** this paper,we propose a black box method,in which the haze is considered an image quality problem without using any physical model such as ***,we propose a novel dehazing equation to combine two mechanisms:interference item and detail enhancement *** interference item estimates the haze information for dehazing the image,and then the detail enhancement item can repair and enhance the details of the dehazed *** on the new equation,we design an antiinterference and detail enhancement dehazing network(AIDEDNet),which is dramatically different from existing dehazing networks in that our network is fed into the haze-free images for ***,we propose a new way to construct a haze patch on the flight of network *** patch is randomly selected from the input images and the thickness of haze is also randomly *** experiment results show that AIDEDNet outperforms the state-of-the-art methods on both synthetic haze scenes and real-world haze scenes.