Bayesian Probabilistic Model for Different Noises Removal
نویسندگان
چکیده
Visual information transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmission is often corrupted with noise. The received image needs processing before it can be used in applications. Image noise removal involves the manipulation of the image data to produce a visually high quality image. 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.
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