Comparision of Various Noise Removals Using Bayesian Framework

A noise is introduced in the transmission medium due to a noisy channel, errors during the measurement process and during quantization of the data. For digital storage each element in the imaging chain such as lenses, film, digitizer, etc. contributes to the degradation. Image noise removal is often used in the field of photography or publishing where an image was somehow degraded but needs to be improved before it can be printed. This paper reviews the Bayesian Estimation process for statistical signal processing. Different noise models including additive and multiplicative types are used. They include Gaussian noise, salt and pepper noise, speckle noise and Poisson noise. Selection of the denoising algorithm is application dependent. Hence, it is necessary to have knowledge about the noise present in the image so as to select the appropriate noise removal algorithm. The filtering approach has been proved to be the best when the image is corrupted with salt and pepper noise. The wavelet based approach finds applications in denoising images corrupted with Gaussian noise. In the case where the noise characteristics are complex, the multifractal approach can be used. Bayesian estimation process is used to optimize the removal of Poisson noise. A quantitative measure of comparison is provided by the signal to noise ratio of the image.