A new image denoising method based on Curvelet transform and histogram segmentation is proposed. This paper first explores the concept and the propertites of the Curvelet transform for curved singularities analysis then applies Curvelet transform and histogram segmentation to estimate optimum threshold for image denoising. In the simulations, the Wrap (Wrapping-based transform) algorithm was used to realize the Curvelet transform, which adds a wrap step to the Unequally Spaced Fast Fourier Transform (USFFT) method. The simulation results show the denoising effectiveness of the proposed method, show that Curvelet transform has a better denoising result and a certain increase in PSNR (Peak Signal-to-Noise Ratio), especially for the images those contain curved singularities
