Though　existing　state-of-the-art　denoising　algorithms,　such　as　BM3D,　LPG-PCA　and　DDF,　obtain　remarkable　results,　these　methods　are　not　good　at　preserving　details　at　high　noise　levels,　sometimes　even　introducing　non-existent　artifacts.　To　improve　the　performance　of　these　denoising　methods　at　high　noise　levels,　a　generic　denoising　framework　is　proposed　in　this　paper,　which　is　based　on　guided　principle　component　analysis　(GPCA).　The　propose　framework　can　be　split　into　two　stages.　First,　we　use　statistic　test　to　generate　an　initial　denoised　image　through　back　projection,　where　the　statistical　test　can　detect　the　significantly　relevant　information　between　the　denoised　image　and　the　corresponding　residual　image.　Second,　similar　image　patches　are　collected　to　form　different　patch　groups,　and　local　basis　are　learned　from　each　patch　group　by　principle　component　analysis.　Experimental　results　on　natural　images,　contaminated　with　Gaussian　and　non-Gaussian　noise,　verify　the　effectiveness　of　the　proposed　framework.　(C)　2017　Elsevier　Inc.　All　rights　reserved.