High　Dynamic　Range　(HDR)　images　have　more　powerful　image　expression　capabilities　and　better　visual　quality　than　Low　Dynamic　Range　(LDR)　images,　which　can　sufficiently　represent　real-world　scenes,　and　will　be　widely　used　in　the　field　of　film　and　television　in　future.　However,　it　is　a　very　challenging　task　to　generate　an　HDR　image　from　a　single-exposure　LDR　image.　In　this　work,　we　propose　a　novel　learning-based　network,　DEUNet,　to　reconstruct　single-frame　HDR　image　with　simultaneous　denoising　and　detail　reconstruction.　The　proposed　framework　consists　of　two　feature　extraction　branches,　which　can　learn　the　brightness　information　and　texture　information　separately　for　HDR　image　reconstruction.　Each　network　branch　is　based　on　the　UNet　network　structure　and　the　two　branches　are　interacted　via　spatial　feature　transformation.　As　a　result,　the　proposed　network　can　make　full　use　of　the　multi-scale　information　at　different　levels　of　the　image.　In　addition　to　the　two　encoding　branches　for　feature　extraction,　the　proposed　network　consists　of　another　decoding　network　for　fusing　image　brightness　information　and　texture　information,　and　a　weighting　network　that　selectively　preserves　most　useful　information.　Compared　with　state-of-the-art　methods,　DEUNet　can　better　reduce　image　noise　while　reconstructing　the　details　in　both　the　high　and　the　low　exposure　areas.　Experiments　have　shown　that　the　proposed　method　achieves　state-of-the-art　performance　on　public　datasets,　indicating　the　effectiveness　of　the　proposed　method　in　this　study.　©　2024　Elsevier　Ltd