【摘 要】
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Removing the smog from digital images is a chal-lenging pre-processing tool in various imaging systems.There-fore,many smog removal(i.e.,desmogging)models are pro-posed so far to remove the effect of smog from images.The desmogging models are based upon a
【机 构】
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College of Computing Science and Information Technogy,Teerthanker Mahaveer University,Moradabad 2440
论文部分内容阅读
Removing the smog from digital images is a chal-lenging pre-processing tool in various imaging systems.There-fore,many smog removal(i.e.,desmogging)models are pro-posed 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 atmo-spheric veil from a single smoggy image.Therefore,many prior based restoration models are proposed in the literature to esti-mate the transmission map and an atmospheric veil.However,these models utilized computationally extensive minimization of an energy function.Also,the existing restoration models suf-fer 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 out-performs the existing desmogging models in terms of various performance metrics.
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