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TITLE : Denoising Various Imaging Modalities using Wavelets, Ridgelets and Curvelets: A Comparative Analysis  
AUTHORS : Mary Sugantharathnam D      Dr.Manimegalai.D            
DOI : http://dx.doi.org/10.18000/ijies.30140  
ABSTRACT :

Image De-noising is a problem of prime importance in the field of Image Processing ranging from Medical Imaging to Satellite imaging. The main purpose of an Image de-noising algorithm is to reduce the noise level to improve both the interpretability and visual aspect of the images. This paper propose to indicate the suitability of different Multi-resolution transforms, viz Wavelets, Ridgelets and Curvelets in de-noising various imaging modalities corrupted by Random noise, Gaussian noise, Speckle Noise, Salt and Pepper Noise and Poisson noise. Though the comparative study is based on various imaging modalities, due relevance is given to medical images like Computed Tomography (CT), Magnetic resonance Imaging (MRI) and X-ray images. Experiments are conducted on various image data sets namely Natural, Satellite and Medical Images with the Multi-resolution transforms using two existing thresholding strategies, namely Soft and Hard Thresholding. A comprehensive evaluation of three different types of Multiresolution transforms corrupted with different types of noise is provided and the quality of de-noising is measured in terms of Peak Signal to Noise ratio (PSNR). Experimental results indicate that the Curvelets reveal superior performance over wavelets and Ridgelets in terms of PSNR value and perceptible quality.

Keywords :Curvelet, De-noising, Ridgelet, Threshold, Wavelet
 
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