Most　existing　visual　saliency　analysis　algorithms　assume　that　the　input　image　is　clean　and　does　not　have　any　disturbances.　However,　this　situation　is　not　always　the　case.　In　this　paper,　we　provide　an　extensive　evaluation　of　visual　saliency　analysis　algorithms　in　noisy　images.　We　analyze　the　noise　immunity　of　saliency　analysis　algorithms　by　evaluating　the　performances　of　the　algorithms　in　noisy　images　with　increasing　noise　scales　and　by　studying　the　effects　of　applying　different　denoising　methods　before　performing　saliency　analysis.　We　use　10　state-of-the-art　saliency　analysis　algorithms　and　7　typical　image　denoising　methods　on　4　eye　fixation　datasets　and　2　salient　object　detection　datasets.　Our　experiments　show　that　the　performances　of　saliency　analysis　algorithms　decrease　with　increasing　image　noise　scales　in　general.　An　exception　is　that　the　nonlinear　features　(NF)　integrated　algorithm　shows　good　noise　immunity.　We　also　find　that　image　denoising　methods　can　greatly　improve　the　noise　immunity　of　the　algorithms.　Our　results　show　that　the　combination　of　NF　and　Median　denoising　method　works　best　on　eye　fixation　datasets　and　the　combination　of　saliency　optimization　(SO)　and　color　block-matching　and　3D　filtering　(C-BM3D)　method　works　best　on　salient　object　detection　datasets.　The　combination　of　SO　and　Average　denoising　method　works　best　for　applications　wherein　time　efficiency　is　a　major　concern　for　both　types　of　datasets.