High　dynamic　range　(HDR)　image　shows　richer　scene　brightness　and　details　for　better　visual　effects　than　conventional　low　dynamic　range　(LDR)　images　since　they　utilize　more　bits　to　express　pixel　values.　With　little　input　information,　the　challenge　of　HDR　reconstruction　is　to　recover　the　details　lost　in　the　under-　/over-exposed　regions　of　an　image.　The　majority　of　current　methods　for　single-frame　HDR　reconstruction　fail　to　focus　on　image　denoising　and　color　balance.　In　this　work,　we　address　these　difficulties　by　proposing　the　notion　of　extracting　image　luminance　features　and　texture　features　separately.　The　method　is　based　on　a　dual-input　channel　encoder-decoder　structure　and　utilizes　a　spatial　feature　transform　module　to　implement　information　interaction　between　both　input　branches　at　different　feature　scales.　In　addition,　our　proposed　network　includes　a　weighting　network　to　preserve　useful　information　about　the　image　selectively.　Through　both　quantitative　and　qualitative　experiments,　we　demonstrate　the　effectiveness　of　the　components　proposed　in　the　network.　In　comparison　to　other　existing　mainstream　methods　in　the　field　on　publicly　available　datasets,　we　have　demonstrated　that　the　proposed　method　enables　noise　reduction　while　recovering　the　lost　image　details.　The　experimental　results　show　that　our　method　achieves　state-of-the-art　performance　in　the　task　of　single-frame　HDR　reconstruction.　The　code　is　available　at　https://github.com/AMSTL-PING/DEUNet-HDRI.git　©　2023　Copyright　held　by　the　owner/author(s).