The　goal　of　an　image　denoising　algorithm　is　to　preserve　the　details　of　clean　images　while　reducing　the　noise　in　noisy　images.　Some　existing　image　denoising　algorithms　preserve　the　details　by　using　external　information.　However,　external　information　needs　to　be　obtained　from　external　images　or　regions　similar　to　the　noisy　images　or　regions.　In　this　letter,　we　propose　a　region-aware　image　denoising　algorithm　(RAID)　by　exploring　parameter　preference.　The　proposed　RAID　algorithm　is　based　on　the　observation　that　different　regions　of　noisy　images　prefer　denoised　results　that　differ　due　to　being　obtained　with　different　denoising　parameters.　The　RAID　algorithm,　first,　measures　the　extent　of　preferences　of　denoising　parameters　for　different　image　regions.　Then,　it　combines　the　denoised　results　obtained　by　using　the　various　denoising　parameters　according　to　the　extent　of　preferences　determined　in　the　previous　step　to　get　the　final　denoised　image.　Experimental　results　show　that　the　proposed　RAID　algorithm　can　be　combined　with　some　existing　image　denoising　algorithms　to　improve　their　denoising　performance.　Our　denoised　results　are　better　at　preserving　the　details　while　reducing　the　noise　than　existing　algorithms　which　use　the　same　parameter　for　the　whole　image.