Journal of Computer Science and Applications. ISSN 2231-1270 Volume 5, Number 1 (2013), pp. 31-37 © International Research Publication House http://www.irphouse.com Image Denoising Based on Curvelet Transforms and its Comparative Study with Basic Filters Kanika Sharma #1 and Kiran Jyoti #2 #1 Department CE, SRSGPCG Ludhiana, India. #2 Department CSE, GNE, Ludhiana, India. E-mail: 1 kanikarieit@yahoo.com, 2 kiranjyoti@yahoo.com Abstract Image denoising is basic work for image processing, analysis and computer vision. This Work proposes a Curvelet Transformation based image denoising, which is combined with the low pass filtering and thresholding methods in the transform domain. Through simulations with images contaminated by white Gaussian noise, this scheme exhibits better performance in both PSNR (Peak Signal-to-Noise Ratio) and visual effect as compared to basic filters. Curvelet transformation is a multi-scale transformation technique which is most suitable for the objects with curves. Introduction Visual information transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmission is often corrupted with noise. The received image needs processing before it can be used in applications. Image denoising involves the manipulation of the image data to produce a visually high quality image. This thesis reviews the existing denoising agorithms, such as filtering approach, Curvelet approach and performs their comparative study. Different noise models including additive and multiplicative types are used. They include Gaussian noise, salt and pepper noise and speckle noise.Selection of the denoising algorithm is application dependent. Hence,it is necessary to have knowledge about the noise present in the image so as to select the appropriate denoising algorithm.The filtering approach has been proved to be the best when the image is corrupted with salt and pepper noise. The curvelet based approach finds applications in denoising images corrupted with Gaussian noise and speckle noise.