Optical EngineeringAdaptive iterative image restoration with reduced computational load
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In this paper, methods for reducing the computational load of an adaptive iterative image restoration algorithm while producing a restored image of high visual quality are proposed. These methods are based on a class of iterative restoration algorithms that exhibit a first- or higherorder convergence, and some of them consist of on-line and off-line cornputational parts. Since only the first-order or linear algorithm can take a computationally feasible adaptive formulation for image restoration, iterative algorithms that combine the linear and higher-order algorithms are proposed. These algorithms converge to the weighted minimum norm least-squares solution with significantly less computational load cornpared to the adaptive linear algorithm. The quality of the adaptively restored image depends on the choice of the weight coefficients, which are evaluated based on the spatial activity of the image. Various methods for computing the local spatial activity in the image are proposed. These methods are shown to produce visually better restoration results. Also, a method for computing the weight coefficients only at the edges is proposed, which results in additional computational savings. Finally, experimental results are presented and the various restoration methods are compared with respect to their computational complexity, the mean squared error, and the visual quality of the restored images.