In today's modem era the use and applications of image processing is increasing day by day. Image enhancement being the most crucial and very early step in image processing has its importance. Curvelet Transform (CT) offers a promising solution for representation, storage and processing of image curved features by means of Ridgelet analysis. So long as in the area of image processing, Wavelet Transform (Vv'T) has been an alternative for noise removal methodologies but since Wavelets shows their inefficiency while dealing with image edge and curved features, the term Curvelet Transform came into existence. Curvelet Transform in Image denoising is a well-established technique in today's era where we face a lot of challenges while dealing with huge amount of data relating to image processing applications. Curvelet Transform has the capability to deal with higher dimensional images having typical curved features and smooth areas along those curves, to do so curvelet makes use of ridgelet analysis after decomposing image into different subbands followed by smooth partitioning so that a curve may become nearly approximate to a straight line. An efficient image denoising technique must remove the noise thereby maintaining the important image features, so the main issue remains the selection and application of appropriate threshold. . While the technologies for acquiring images continue to improve, resulting images of higher and higher resolution and quality are expected, Rician noise still remains. The main trouble detoriating the quality while removing this noise from images remains one of the main approach inside the examination of imaging. Thus, extending the application of curvelet transform, aiming to filter the noisy image and restorating the losses in images. In particular, the aim is to develop an efficient artifact, free edge preserving vhr image denoising method using curvelet transform, assess and compare their performance in such a way to improve the reconstructed image quality.