When　taking　photos　in　dim-light　environments,　due　to　the　small　amount　of　light　entering,　the　shot　images　are　usually　extremely　dark,　with　a　great　deal　of　noise,　and　the　color　cannot　reflect　real-world　color.　Under　this　condition,　the　traditional　methods　used　for　single　image　denoising　have　always　failed　to　be　effective.　One　common　idea　is　to　take　multiple　frames　of　the　same　scene　to　enhance　the　signal-to-noise　ratio.　This　paper　proposes　a　recurrent　fully　convolutional　network　(RFCN)　to　process　burst　photos　taken　under　extremely　low-light　conditions,　and　to　obtain　denoised　images　with　improved　brightness.　Our　model　maps　raw　burst　images　directly　to　sRGB　outputs,　either　to　produce　a　best　image　or　to　generate　a　multi-frame　denoised　image　sequence.　This　process　has　proven　to　be　capable　of　accomplishing　the　low-level　task　of　denoising,　as　well　as　the　high-level　task　of　color　correction　and　enhancement,　all　of　which　is　end-to-end　processing　through　our　network.　Our　method　has　achieved　better　results　than　state-of-the-art　methods.　In　addition,　we　have　applied　the　model　trained　by　one　type　of　camera　without　fine-tuning　on　photos　captured　by　different　cameras　and　have　obtained　similar　end-to-end　enhancements.