The　electroencephalogram　(EEG)　serves　as　a　significant　tool　in　the　realms　of　clinical　medicine,　cerebral　investigation,　and　neurological　disorders　research.　However,　the　EEG　records　we　obtain　are　often　easily　contaminated　by　various　artifacts,　which　can　blur　or　distort　the　underlying　EEG　signals　and　make　data　interpretation　difficult.　Generally　speaking,　removing　EEG　artifacts　is　considered　an　essential　step　in　brain　signal　analysis.　Therefore,　removing　artifacts　is　crucial　for　obtaining　accurate　and　reliable　EEG　signals　for　subsequent　analysis.　Recently,　deep　learning　techniques　have　found　widespread　application　across　various　domains　for　denoising　tasks,　including　image　denoising　and　EEG　denoising.　Many　advanced　algorithms　have　been　developed　in　image　denoising,　which　has　achieved　good　results　in　enhancing　low-quality　images.　Moreover,　it　has　shown　superior　performance　in　EEG　denoising.　In　contrast,　few　people　have　devoted　themselves　to　studying　EEG　denoising,　and　existing　convolutional　neural　network　EEG　denoising　methods　still　have　problems　of　overfitting　and　poor　denoising　effect　in　Electromyograph(EMG)　and　ElectroOculoGram(EOG)　artifact　removal.　Therefore,　this　paper　proposes　a　method　called　DWINet　(De-artifacting　with　Image-based　Network　for　EEG　Signals)　based　on　an　image　dehazing　network　DRHNet　for　removing　artifacts　from　EEG　signals.　Specifically,　our　approach　DWINet,　addresses　the　de-artifacting　issue　in　EEG　signals　by　converting　it　as　an　image　dehazing　problem　and　utilizes　the　image　dehazing　capability　of　DRHNet　to　enhance　the　denoising　performance　of　EEG　signals.　Experimental　results　demonstrate　that　the　proposed　method　outperforms　the　compared　algorithms　in　removing　the　ocular　artifact　in　EEG　signals　and　exhibits　higher　accuracy　and　robustness.　©　2024,　J.　Network　Intell.　All　rights　reserved.