Early　screening　and　diagnosis　of　cervical　precancerous　lesions　are　very　important　to　prevent　cervical　cancer.　High-quality　colposcopy　images　will　help　doctors　make　faster　and　more　accurate　diagnoses.　To　tackle　the　problem　of　low　image　quality　caused　by　complex　interference　during　colposcopy　operation,　this　paper　proposed　a　conditional　entropy　generative　adversarial　networks　framework　for　image　enhancement.　A　decomposition　network　based　on　Retinex　theory　is　constructed　to　obtain　the　reflection　images　of　the　low-quality　images,　then　the　conditional　generative　adversarial　network　is　used　as　the　enhancement　network.　The　low-quality　images　and　the　decomposed　reflection　images　are　both　input　the　enhancement　network　for　training,　and　the　conditional　entropy　distance　is　used　as　a　part　of　the　loss　of　the　conditional　generative　adversarial　network　to　alleviate　the　over-fitting　problem　during　the　training　process.　The　test　results　show　that　compared　with　published　methods,　the　proposed　method　of　this　paper　can　significantly　improve　the　image　quality,　and　can　enhance　the　colposcopy　image　while　retaining　image　details.