To　address　the　problems　of　high-frequency　information　missing　and　increased　noise　in　images　generated　by　existing　image　super-resolution　reconstruction　algorithms，this　paper　proposes　an　image　super-resolution　reconstruction　model　based　on　convolutional　sparse　coding　and　generative　adversarial　networks.　Firstly，convolutional　networks　are　employed　to　implement　sparse　coding　and　obtain　a　sparse　representation　of　the　image，which　makes　full　use　of　the　prior　information　of　the　image　and　effectively　avoids　the　problems　of　high-frequency　information　missing　and　increased　noise　in　the　reconstructed　image.　After　obtaining　the　sparse　representation　of　the　low-resolution　image，the　sparse　representation　is　reconstructed　by　the　reconstruction　module　to　obtain　the　super-resolution　image.　Subsequently，　the　discriminator　discriminates　the　reconstructed　image　to　alleviate　the　problem　that　the　reconstructed　image　tends　to　be　smooth　due　to　the　PSNR-dominated　algorithm.　After　continuous　adversarial　training，the　final　generated　super-resolution　images　are　made　to　have　better　visual　effects.　The　super-resolution　reconstruction　experiments　are　performed　on　Set5，Set14，BSD100　and　Urban100　general　test　datasets　at　2×　and　4×　and　compared　with　Bicubic，SRGAN，EDSR　and　ESRGAN　methods.　Compared　with　ESRGAN，the　average　PSNR　improvement　is　about　0.　702　8　dB，the　average　SSIM　improvement　is　about　0.　047，and　the　average　LPIPS　improvement　is　0.　016　on　the　four　datasets.　Experimental　results　show　that　the　proposed　model　is　highly　competitive　and　enables　the　recovery　of　more　fine-texture　details　with　better　definition.　©　2023,　Science　Press.　All　rights　reserved.