Study of image denoising using curvelet transform

The images usually bring different kinds of noise in the process of receiving, coding and Transmission. In our implementation the Curvelet transform is used for de-noising of image. Two digital implementations of the Curvelet transform the Unequally Spaced Fast Fourier Transform (USFFT) and the Wrapping Algorithm are used to de-noise images degraded by different types of noises such as Gaussian, Salt and Pepper, Random, Speckle and Poisson noise. This thesis aims at the effect the Curvelet transform has in Curve-let shrinkage assuming different types of noise models. A signal to noise ratios a measure of the quality of de-noising was preferred. The experimental results show that the normal Curvelet shrinkage approach fails to remove Poisson noise in medical images