In　order　to　solve　the　problems　of　low　contrast,　global　darkness　and　noise　amplification　in　some　hyperspectral　and　remote-sensing　images,　a　new　image　fuzzy　enhancement　method　based　on　contourlet　transform　(CT)　domain　is　proposed.　The　algorithm　includes　the　following　four　steps.　Firstly,　the　cyclic　translation　method　is　used　to　suppress　the　pseudo-Gibbs　phenomenon　caused　by　the　lack　of　translation　invariance　of　the　CT.　Secondly,　a　nonlinear　gain　function　is　designed　to　enhance　and　suppress　the　high-frequency　coefficients　adaptively.　Meanwhile,　the　multi-scale　Retinex　with　mixed　gray　function　is　used　to　process　the　low-frequency　sub-band　coefficients.　Then,　the　inverse　translation　and　linear　averaging　and　the　inverse　CT　are　performed　on　the　enhanced　coefficients,　and　finally　the　improved　fuzzy　contrast　is　used　to　enhance　the　texture　and　edge　of　the　image　globally.　The　experimental　results　show　that　the　proposed　method　can　make　the　image　texture　details　more　prominent,　and　enhance　the　overall　visual　effect　of　the　images.　Furthermore,　the　absolute　mean　difference　and　PSNR　of　images　are　also　greatly　improved　.