Recently　developed　dual　domain　image　denoising　(DDID)　algorithm　and　its　variants,　such　as　dual　domain　filter　(DDF),　achieve　remarkable　results　by　combining　bilateral　filter　with　frequency-based　method.　However,　this　kind　of　algorithms　require　large　patches　to　guarantee　the　denoising　performance　and　most　of　them　produce　ringing　artifacts　due　to　the　Gibbs　phenomenon　induced　by　high-contrast　details.　To　address　these　issues,　we　propose　a　Foveated　Nonlocal　Dual　Denoising　(FNDD)　algorithm　by　unifying　foveated　nonlocal　means　and　frequency-based　methods.　In　this　way,　the　ability　to　preserve　the　high-contrast　details　is　noticeably　improved　by　exploiting　foveated　self-similarity　(patch　similarity)　instead　of　pixel　similarity,　thus　leading　to　void　of　artifacts.　Moreover,　we　propose　an　entropy-based　back　projection　step　for　compensating　the　detail　loss　to　further　improve　the　performance.　Experimental　results　validate　that　FNDD　significantly　outperforms　DDID　in　terms　of　both　quantitative　metrics　and　subjective　visual　quality　under　much　smaller　patches,　and　even　achieves　comparable　results　against　state-of-the-art　competitors.　©　2017　IEEE.