The Curvelet Approach for Denoising in various Imaging Modalities using Different Shrinkage Rules

The images usually bring different kinds of noise in the process of receiving, coding and transmission. In this paper the Curvelet transform is used for de-noising of image. Two digital implementations of the Curvelet transform (a multiscale transform) viz 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 Random, Gaussian, Salt and Pepper, Speckle and Poisson noise. This paper aims at the effect the Curvelet transform has in Curvelet shrinkage assuming different types of noise models. A signal to noise ratio as a measure of the quality of de-noising was preferred. The experimental results show that the conventional Curvelet shrinkage approach fails to remove Poisson noise in medical images.