Study of image denoising using curvelet transform

Abstract

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.