This　paper　proposes　a　new　neural　network　for　enhancing　underexposed　images.　Instead　of　the　decomposition　method　based　on　Retinex　theory,　we　introduce　smooth　dilated　convolution　to　estimate　global　illumination　of　the　input　image,　and　implement　an　end-to-end　learning　network　model.　Based　on　this　model,　we　formulate　a　multi-term　loss　function　that　combines　content,　color,　texture　and　smoothness　losses.　Our　extensive　experiments　demonstrate　that　this　method　is　superior　to　other　methods　in　underexposed　image　enhancement.　It　can　cover　more　color　details　and　be　applied　to　various　underexposed　images　robustly.　©　2020　IEEE.