Performance Evaluation of Different Image Denoising Methods

An image is inevitably corrupted by noise in its acquisition or transmission. Noises in an image can degrade severely the follow-up image processing tasks, such as image feature extraction, coding, segmentation, and target detection. Thus noise reduction becomes a very important image pre-processing for improving the quality of image and meeting the needs of higher level processing tasks. In this paper we develop an algorithm for three different denoising methods: Median filter, wavelet domain based denoising and approximation part obtained by wavelet domain method is again filtered by median filter to get better denoised result. Image is considered as noised by Gaussian, speckle and salt & pepper noises. The results shows that if the image are affected by Gaussian and speckle noise then the method in which approximation part is filtered by median filter will perform better than individual median and wavelet domain method.