ABSTRACT
Image denoising refers to the restoration of an image contaminated by additive white Gaussian noise (AWGN). Just like AWGN has served as the simplest situation in modeling channel degradation in digital communication, image denoising represents the simplest task in image restoration and therefore has been extensively studied by several technical communities. It should be noted that the study of the more general problem of signal denoising dates back to at least Norbert Wiener in the 1940s. The celebrated Wiener filter provides the optimal solution to the recovery of Gaussian signals contaminated by AWGN. The derivation of Wiener filtering, based on the so-called orthogonality principle, represents an elegant solution and the only known situation where constraining to linear solutions does not render any sacrifice on the performance. Therefore, at least in theory the problem of image denoising can be solved if we can reduce it to a problem that satisfies the assumptions behind the Wiener filtering theory. The challenge of image denoising ultimately boils down to the art of modeling images.
