Magnetic　resonance　(MR)　imaging　plays　an　important　role　in　clinical　and　brain　exploration.　However,　limited　by　factors　such　as　imaging　hardware,　scanning　time,　and　cost,　it　is　challenging　to　acquire　high-resolution　MR　images　clinically.　In　this　article,　fine　perceptive　generative　adversarial　networks　(FP-GANs)　are　proposed　to　produce　super-resolution　(SR)　MR　images　from　the　low-resolution　counterparts.　By　adopting　the　divide-and-conquer　scheme,　FP-GANs　are　designed　to　deal　with　the　low-frequency　(LF)　and　high-frequency　(HF)　components　of　MR　images　separately　and　parallelly.　Specifically,　FP-GANs　first　decompose　an　MR　image　into　LF　global　approximation　and　HF　anatomical　texture　subbands　in　the　wavelet　domain.　Then,　each　subband　generative　adversarial　network　(GAN)　simultaneously　concentrates　on　super-resolving　the　corresponding　subband　image.　In　generator,　multiple　residual-in-residual　dense　blocks　are　introduced　for　better　feature　extraction.　In　addition,　the　texture-enhancing　module　is　designed　to　trade　off　the　weight　between　global　topology　and　detailed　textures.　Finally,　the　reconstruction　of　the　whole　image　is　considered　by　integrating　inverse　discrete　wavelet　transformation　in　FP-GANs.　Comprehensive　experiments　on　the　MultiRes_7T　and　ADNI　datasets　demonstrate　that　the　proposed　model　achieves　finer　structure　recovery　and　outperforms　the　competing　methods　quantitatively　and　qualitatively.　Moreover,　FP-GANs　further　show　the　value　by　applying　the　SR　results　in　classification　tasks.