Iterative Image Restoration

This chapter describes the application of the successive approximations-based class of iterative algorithms to the problem of restoring a noisy and blurred image. It presents and analyzes the simpler forms of the algorithm and describes an iteration-adaptive form of the algorithm following a deterministic approach but also a hierarchical Bayesian approach. In addition, two other inverse problems-the removal of blocking artifacts and the enhancement of resolution-have been described in the chapter. The success in solving any recovery problem depends on the amount of the available prior information. This information refers to the properties of the original image, the degradation system, and the noise process. After the degradation model is established, the next step is the formulation of a solution approach. This might involve the stochastic modeling of the input image, the determination of the model parameters, and the formulation of a criterion to be optimized. Alternatively, it might involve the formulation of a functional expression to be optimized subject to the constraints imposed by the prior information.