Group　sparse　coding　(GSC)　is　a　powerful　mechanism　that　has　achieved　great　success　in　many　low-level　vision　tasks,　showing　great　potential　in　image　denoising.　Traditional　group　sparse　coding　generally　uses　overcomplete　dictionaries　and　l1-norm　to　regularize　sparse　coefficients.　But　this　is　only　an　estimate　of　the　solution,　which　cannot　obtain　a　sparse　solution　and　has　a　high　computational　cost.　In　this　paper,　we　use　a　GSC　framework　with　adaptive　dictionary　learning　for　image　denoising.　In　order　to　improve　the　accuracy　of　obtaining　sparse　coefficients,　the　dictionary　used　in　this　paper　is　learned　from　the　input　image,　which　can　be　obtained　by　applying　SVD　once　for　each　patch　group.　Then　use　ADMM　algorithm　to　solve　the　objective　function.　Experimental　results　show　that　the　PSNR　value　of　our　approach　not　only　is　competitive　with　many　advanced　image　denoising　methods　but　also　achieves　better　visual　effects.　©　2022　IEEE.