In　the　realm　of　image　super-resolution,　learning-based　methods　have　made　significant　progress.　However,　limited　computational　resources　still　restrict　their　application.　This　prompts　us　to　develop　an　efficient　method　for　achieving　effective　image　super-resolution.　In　this　letter,　we　propose　a　novel　adaptive　feature　selection　modulation　network　(AFSMNet)　tailored　for　efficient　image　super-resolution.　Specifically,　we　design　feature　modulation　blocks,　which　include　the　adaptive　feature　selection　modulation　(AFSM)　module　and　the　self-gating　feed-forward　network　(SFN).　The　AFSM　module　dynamically　computes　the　importance　of　each　feature　channel.　For　channels　with　differing　levels　of　importance,　we　employ　distinct　processing　strategies,　thereby　concentrating　the　computational　resources　of　the　network　on　the　more　critical　features　as　much　as　possible.　This　approach　facilitates　the　maintenance　of　a　low　computational　cost　without　compromising　performance.　The　SFN　restricts　the　flow　of　irrelevant　feature　information　within　the　network　through　a　simple　gating　mechanism.　In　this　way,　our　method　achieves　efficient　and　effective　image　super-resolution.　Extensive　experiment　results　show　that　the　proposed　method　achieves　a　better　trade-off　between　reconstruction　performance　and　computational　efficiency　compared　to　the　current　state-of-the-art　lightweight　super-resolution　methods.