Due　to　the　hazy　weather　and　the　long-distance　imaging　path,　the　captured　remote　sensing　image　(RSI)　may　suffer　from　detail　loss　and　noise　pollution.　However,　simply　applying　dehazing　operation　on　a　noisy　hazy　image　may　result　in　noise　amplification.　Therefore,　in　this　article,　we　propose　joint　blind　denoising　and　dehazing　for　RSI　recovery　and　enhancement　to　address　this　problem.　First,　we　propose　an　efficient　and　effective　noise　level　estimation　method　based　on　quad-tree　subdivision　and　integrate　it　into　fast　and　flexible　denoising　convolutional　neural　network　for　blind　denoising.　Second,　a　multiscale　guided　filter　decomposes　the　denoised　hazy　image　into　base　and　detailed　layers,　separating　the　initial　details.　Then,　the　dehazing　procedure　using　the　corrected　boundary　constraint　is　implemented　in　the　base　layer,　while　a　nonlinear　sigmoid　mapping　function　enhances　the　detailed　layers.　The　last　step　is　to　fuse　the　enhanced　detailed　layers　and　the　dehazed　base　layer　to　get　the　final　result.　Using　both　synthetic　remote　sensing　hazy　image　(RSHI)　datasets　and　real-world　RSHI,　we　perform　comprehensive　experiments　to　evaluate　the　proposed　method.　Results　show　that　our　method　is　superior　to　well-known　methods　in　both　dehazing　and　joint　denoising　and　dehazing　tasks.　©　2008-2012　IEEE.