A Survey on Types of Noise and Image Denoising Techniques.docx
Now a days, information transmitted in the form of digital images is becoming a major way of communication in the modern age, but the image obtained after transmission is mostly corrupted with noise. The received image needs to processing before it can be used in applications. The important property of a good image denoising model is that it should completely remove or reduce noise as far as possible as well as preserve edges. Image denoising involves the preprocessing of the obtained image data to produce a visually high quality image. This paper reviews the existing denoising algorithms, such as filtering methods, wavelet based techniques, and multifractal approach, and also performs their comparative study. Different noise models including additive as well as multiplicative types are used, such as Gaussian noise, speckle noise, salt and pepper noise and Brownian noise. Selection of the denoising algorithm is depends on application. Hence, it is necessary to have knowledge about the type of noise present in the selected image to select the appropriate denoising algorithm. The image filtering techniques has been proved to be the best when the image is corrupted with salt and pepper noise. The wavelet based technique finds applications in denoising images corrupted with Gaussian noise. The multifractal technique can be used,In the case where the noise characteristics are complex. A quantitative measure of comparison is provided by the signal to noise ratio(SNR) of the image.