Unsupervised Low Illumination Image Enhancement Based on Channel Attention and Illumination Weights
Some existing unsupervised low light image enhancement methods may reduce the brightness of highlights in areas with insufficient image exposure,resulting in artifacts in the enhanced image;A single TV loss cannot distinguish the details of the lighting feature map,and it will also ignore areas with prominent differences in brightness at the edges of the lighting feature map,leading to the occurrence of halo phe-nomena.To this end,a unsupervised low light image enhancement method VARRNet based on channel attention and lighting weight is pro-posed.Firstly,VARRNet converts images into HSV space and combines V space with Retinex theory to avoid information loss;Secondly,in order to prevent the generation of artifacts during the brightness enhancement process,a brightness estimation network was designed to intro-duce channel attention ECA to allocate the weights of input feature maps,in order to restore the brightness of underexposed areas and effective-ly maintain the brightness of highlight areas;Finally,in the brightness estimation network,TV loss and lighting component weight are com-bined to preserve the rich detail information of the enhanced feature map and eliminate halos at strong edges.Compared with five popular low light image enhancement methods,VARRNet achieved better visualization results in brightness enhancement,detail preservation,color resto-ration,artifact suppression,and halo removal.
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