Image smog restoration using oblique gradient profile prior and energy minimization  

作　　者：Ashok KUMAR Arpit JAIN 

机构地区：[1]College of Computing Science and Information Technogy,TeerthankerMahaveer University,Moradabad 244001,India

出　　处：《Frontiers of Computer Science》2021年第6期147-153,共7页中国计算机科学前沿（英文版）

基　　金：The authors would like to thank their organizations especially Teerthanker Mahaveer University,Moradabad,India to provide suitable time and resources to successfully finish this research work.

摘　　要：Removing the smog from digital images is a challenging pre-processing tool in various imaging systems.Therefore,many smog removal(i.e.,desmogging)models are proposed so far to remove the effect of smog from images.The desmogging models are based upon a physical model,it means it requires efficient estimation of transmission map and atmospheric veil from a single smoggy image.Therefore,many prior based restoration models are proposed in the literature to estimate the transmission map and an atmospheric veil.However,these models utilized computationally extensive minimization of an energy function.Also,the existing restoration models suffer from various issues such as distortion of texture,edges,and colors.Therefore,in this paper,a convolutional neural network(CNN)is used to estimate the physical attributes of smoggy images.Oblique gradient channel prior(OGCP)is utilized to restore the smoggy images.Initially,a dataset of smoggy and sunny images are obtained.Thereafter,we have trained CNN to estimate the smog gradient from smoggy images.Finally,based upon the computed smog gradient,OGCP is utilized to restore the still smoggy images.Performance analyses reveal that the proposed CNN-OGCP based desmogging model outperforms the existing desmogging models in terms of various performance metrics.

关 键 词：convolutional neural networks desmogging SMOG oblique gradient channel prior 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]