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Image Denoising using Curvelet Transform

In this paper we propose a new method to reduce noise in digital image. Images corrupted by Gaussian Noise are still a classical problem. To reduce the noise or to improve the quality of image we have used two parameters i.e. quantitative and qualitative. For quantity we will compare peak signal to noise ratio (PSNR). Higher the PSNR better the quality of the image. The Curvelet transform is a higher dimensional generalization of the Wavelet transform designed to represent images at different scales and different angles .In this paper we proposed a Curvelet Transformation based image denoising, which is combined with weiner filter in place of the low pass filtering in the transform domain. We demonstrated through simulations with images contaminated by three different noise i.e. Gaussian, salt and Pepper and speckle. Experimental results show that our proposed method gives comparatively higher peak signal to noise ratio (PSNR) value, are much more efficient and also have less visual artifacts compared to other existing methods.