To　remove　image　haze　and　make　haze　image　scene　clear,　we　proposed　an　image　dehazing　network　based　on　multi-scale　feature　extraction　(MSFNet)　in　this　paper.　The　MSFNet　first　directly　performs　feature　extraction　on　hazy　images　with　three　different　resolutions　to　obtain　fine　feature　maps　and　concatenates　them　with　the　rough　feature　maps　extracted　in　the　downsampling　process　for　fusing　and　obtaining　richer　image　information.　Then,　the　fused　feature　maps　are　put　into　a　network　module　composed　of　ResNeXt　building　blocks　for　network　learning.　Next,　the　feature　maps　extracted　by　upsampling　are　sequentially　concatenated　with　the　feature　maps　learned　by　the　ResNeXt　module　for　obtaining　the　residual　image.　Finally,　the　learned　residual　image　is　added　to　the　input　hazy　image　to　obtain　the　image　dehazing　result.　The　experimental　results　on　the　SOTS　dataset　show　that　the　MSFNet　improves　effectiveness　of　image　dehazing.　©　2022,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.