A new image denoising method using a non-Gaussian bivariate model in a Complex Curvelet Transform(CCT) domain is presented.For avoiding the shift-variance and under-sampling during the 1D inverse Fourier transform in the traditional Curvelet transform ,a new Curvelet transform,Complex Curvelet Transform(CCT),is proposed by adopting the complex wavelet transform and reformative Radon transform to replace the traditional wavelet transform and the old Radon transform respectively,which provides a non-aliasing property for the proposed method.Because the inter-scale correlation of a signal coefficient is stronger than those of noise coefficients,the non-Gaussian bivariate model is used for capturing inter-scale correlation of the signal coefficient and for obtaining the denoised coefficient from the noisy image decomposition by a Bayesian MAP estimator.Experimental results show that the Peak Signel Noise Rotio(PSNR) of the proposed algorithm is averagely higher about 2.9 dB and 1.5 dB than those of the traditional Curvelet transform denoising method and Curvelet domain HMT denoising method respectively at all noise levels.The proposed method avoids “scratching” and “embedded blemishes” phenomena in the reconstructed image,and achieves an excellent balance between suppressing noises effectively and preserving image details and edges as many as possible.