Estimating priors in maximum entropy image processing

A class of discrete image-reconstruction and restoration problems is addressed. A brief description is given of the maximum a posteriori (MAP) Bayesian approach with maximum entropy (ME) priors to solve the linear system of equations which is obtained after the discretization of the integral equations which arises in various tomographic image restoration and reconstruction problems. The main problems of choosing an a priori probability law for the image and determining its parameters from the data is discussed. A method simultaneously estimating the parameters of the ME a priori probability density function and the pixel values of the image is proposed, and some simulations which compare this method with some classical ones are given