Low　visibility　in　hazy　weather　causes　the　loss　of　image　details　in　digital　images　captured　by　some　imaging　devices　such　as　monitors.　This　paper　proposes　an　end-to-end　U-Net　based　residual　network　(URNet)　to　improve　the　visibility　of　hazy　images.　The　encoder　module　of　URNet　uses　hybrid　convolution　combining　standard　convolution　with　dilated　convolution　to　expand　the　receptive　field　for　extracting　image　features　with　more　details.　The　URNet　embeds　several　building　blocks　of　ResNet　into　the　junction　between　the　encoder　module　and　the　decoder　module.　This　prevents　network　performance　degradation　due　to　the　vanishing　gradient.　After　considering　large　absolute　difference　on　image　saturation　and　value　components　between　hazy　images　and　haze-free　images　in　the　HSV　color　space,　the　URNet　defines　a　new　loss　function　to　better　guide　the　network　training.　Experimental　results　on　synthetic　hazy　images　and　real　hazy　images　show　that　the　URNet　significantly　improves　the　image　dehazing　effect　compared　to　the　state-of-the-art　methods.　©　2020　Elsevier　B.V.