A Joint Multiscale Algorithm with Auto-adapted Threshold for Image Denoising

Curvelet transform is one of the recently developed multiscale transform, which can well deal with the singularity of line and provides optimally sparse representation of images with edges. But now the image denoising based on curvelet transform is almost used the Monte Carlo threshold, it is not used the feature of images’ curvelet coefficients effectively, so the best result can not be reached. Meanwhile, the wavelet transform codes homogeneous areas better than the curvelet transform. In this paper a joint multiscale algorithm with auto-adapted Monte Carlo threshold is proposed. This algorithm is implemented by combining the wavelet transform and the fast discrete curvelet transform, in which the auto-adapted Monte Carlo threshold is used. Experimental results show that this method eliminate white Gaussian noise effectively, improves Peak Signal to Noise Ratio (PSNR) and realizes the balance between protecting image details and wiping off noise better.