Low-light　imaging　task　aims　to　approximate　low-light　scenes　as　perceived　by　human　eyes.　Existing　methods　usually　pursue　higher　brightness,　resulting　in　unrealistic　exposure.　Inspired　by　Human　Vision　System　(HVS),　where　rods　perceive　more　lights　while　cones　perceive　more　colors,　we　propose　a　Low-light　Degradation　Rectify　Model　(LDRM)　with　color-monochrome　cameras　to　solve　this　problem.　First,　we　propose　to　use　a　low-ISO　color　camera　and　a　high-ISO　monochrome　camera　for　low-light　imaging　under　short-exposure　of　less　than　0.1s.　Short-exposure　could　avoid　motion　blurriness,　while　monochrome　camera　captures　more　photons　than　color　camera.　By　mimicing　HVS,　this　capture　system　could　benefit　low-light　imaging.　Second,　we　propose　an　LDRM　model　to　fuse　the　color-monochrome　image　pair　into　a　high-quality　image.　In　this　model,　we　separately　restore　UV　and　Y　channels　through　chrominance　and　luminance　branches　and　use　monochrome　image　to　guide　the　restoration　of　luminance.　We　also　propose　a　latent　code　embedding　method　to　improve　the　restorations　of　both　branches.　Third,　we　create　a　Low-light　Color-Monochrome　benchmark　(LCM),　including　both　synthetic　and　real-world　datasets,　to　examine　low-light　imaging　quality　of　LDRM　and　the　state-of-the-art　methods.　Experimental　results　demonstrate　the　superior　performance　of　LDRM　with　visually　pleasing　results.　Codes　and　datasets　are　available　at　https://github.com/StephenLinn/LDRM.