Image de-noising is a process for removal of noise in digital image processing where image may be corrupted during its acquisition or transmission. As noise addition reduce the visual performance and computerized analysis so de-noising is applied that helps to retain its quality of the original image after removal of noise. This procedure is traditionally performed in the spatial or frequency domain by filtering. The process of removing noise from the original image is still a demanding problem for researchers. Recently, a lot of methods have been implemented that perform de-noising such as Filtering techniques, Wavelet Transform (DWT) domain, Curvelet transform method. The prime focus of this paper is related to the pre processing of an image done by de-noising before it can be used in applications. In order to achieve these de-noising algorithms, filtering technique and wavelet based de-noising technique are used and performs their comparative study. Noises in form of Gaussian noise ,speckle noise ,salt pepper noise are used. The curvelet based approach has been proved to be the best among all filtering and Dwt based approach of de-noising. A quantitative measure of comparison is provided by the parameters like Peak signal to noise ratio, mean square error of the image.