Image　super-resolution　reconstruction　is　a　research　hotspot　in　the　field　of　computer　vision.　Traditional　image　super-resolution　reconstruction　methods　based　on　deep　learning　mostly　up-sample　low-resolution　images　ignoring　categories　and　instances,　which　will　cause　some　problems　such　as　unrealistic　texture　in　the　reconstructed　images　or　sawtooth　phenomenon　on　the　edge　of　instance.　In　this　manuscript,　we　propose　an　image　super-resolution　reconstruction　method　based　on　instance　spatial　feature　modulation　and　feedback　mechanism.　First,　the　prior　knowledge　of　instance　spatial　features　is　introduced　in　the　reconstruction　process.　Instance　spatial　features　of　low-resolution　images　are　extracted　to　modulate　super-resolution　reconstruction　features.　Then,　based　on　the　feedback　mechanism,　the　modulated　low-resolution　image　features　are　iteratively　optimized　for　the　reconstruction　results,　so　that　the　model　can　finally　learn　instance-level　reconstruction　ability.　Experiments　on　COCO-2017　show　that,　compared　with　traditional　deep　learning-based　image　super-resolution　reconstruction　methods,　the　proposed　method　can　obtain　better　image　reconstruction　results,　and　the　reconstructed　images　have　more　realistic　instance　textures.