Abstract

Visual information transmitted in the form of digital images is becoming a major method of\ncommunication in the modern age, but the image obtained after transmission is often corrupted with noise. The received\nimage needs processing before it can be used in applications. Image denoising involves the manipulation of the image data\nto produce a visually high quality image. This paper reviews the existing denoising algorithms, such as filtering approach,\nwavelet based approach, and multifractal approach, and performs their comparative study. Different noise models\nincluding additive and multiplicative types are used. They include Gaussian noise, salt and pepper noise, speckle noise and\nBrownian noise. Selection of the denoising algorithm is application dependent. Hence, it is necessary to have\nknowledge about the noise present in the image so as to select the appropriate denoising algorithm. The filtering\napproach has been proved to be the best when the image is corrupted with salt and pepper noise. The wavelet based approach\nfinds applications in denoising images corrupted with Gaussian noise. In the case where the noise characteristics are\ncomplex, the multifractal approach can be used. A quantitative measure of comparison is provided by the signal to\nnoise ratio of the image.