Deep　convolutional　neural　networks　have　achieved　state-of-the-art　performance　for　the　removal　of　atmospheric　obscuration.　However,　most　relevant　studies　have　focused　on　eliminating　the　effects　of　atmospheric　obscuration　but　not　on　the　glare　in　images　caused　by　reflected　sunlight.　On　the　basis　of　a　glare　image　formation　model,　we　propose　a　deep　trident　decomposition　network　with　a　large-scale　sun　glare　image　dataset　for　glare　removal　from　single　images.　Specifically,　the　proposed　network　is　designed　and　implemented　with　a　trident　decomposition　module　for　decomposing　an　input　glare　image　into　occlusion,　foreground,　and　coarse　glare-free　images　by　exploring　background　features　from　spatial　locations.　Moreover,　a　residual　refinement　module　is　adopted　to　refine　the　coarse　glare-free　image　into　fine　glare-free　image　by　learning　the　residuals　from　features　of　multiscale　receptive　field.　The　experimental　results　indicated　that　the　proposed　network　significantly　outperforms　state-of-the-art　atmospheric　obscuration　removal　networks　on　the　built　dataset.　©　2000-2011　IEEE.