Images　captured　in　low-light　conditions　are　often　disturbed　by　low-light,　blur　and　noise.　Most　of　the　conventional　image　enhancement　methods　are　less　robust　without　considering　the　effectiveness　of　the　blur　and　noise.　To　enhance　image　equality　under　the　complex　environment,　we　propose　a　novel　image　enhancement　method　based　on　joint　generative　adversarial　network　(GAN)　and　image　quality　assessment　(IQA)　techniques.　GAN　can　be　well　used　for　image　enhancement　in　low-light　case,　but　it　is　not　robust　in　blur　and　noise　case.　IQA　method　uses　CNN　to　evaluate　each　enhanced　image　quality　based　on　some　scores　that　correlates　well　with　the　human　perception.　The　scores　can　guide　the　GAN　learning　for　further　enhancing　the　image　quality.　Instead　of　l2-term　loss　function,　we　define　a　multi-term　loss　function　for　its　minimization　to　create　a　good　image　estimate.　Experimental　results　demonstrate　the　proposed　method　is　more　effective　than　current　state-of-art　methods　in　terms　of　the　quantitative　and　qualitative　evaluation.　©　2018　IEEE.