Recently,　patch　group　collaborative　denoising　algorithm　based　on　image　self-similarity　has　made　rapid　development,　but　how　to　find　similar　patches　quickly　and　accurately　in　a　noisy　environment　is　a　difficult　problem.　The　common　block　matching　algorithm　defines　the　similarity　between　image　patches　through　Euclidean　distance,　which　could　not　measure　the　internal　structure　information　of　image　patches.　A　search　method　for　similar　patches　is　proposed　based　on　fully　convolutional　siamese　network.　Firstly,　the　potential　relationship　between　clean　reference　patch　and　noisy　image　patch　is　learned　through　siamese　network.　Then,　Mahalanobis　distance　is　used　to　measure　the　similarity　between　image　patches　by　using　structural　information　of　image　patches.　Finally,　the　image　is　restored　by　using　similar　patches　collaborative　denoising.　Experiments　show　that　the　PSNR　value　of　the　proposed　algorithm　is　higher　than　GID　by　0.51　dB,　1.02　dB　and　0.20　dB　on　Nam-CC15,　Nam-CC60　and　PolyU　real　image　datasets　respectively.　By　visual　comparisons,　the　proposed　algorithm　can　preserve　more　structural　features　than　the　competing　methods.　©　2022　Institute　of　Computing　Technology.　All　rights　reserved.