A method for optimizing image-recovery algorithms is presented that is based on how well a specified visual task can be performed using the reconstructed images. Visual task performance is numerically assessed by a Monte Carlo simulation of the complete imaging process including the generation of scenes appropriate to the desired application, subsequent data taking, image recovery, and performance of the stated task bad on the final image. This method is used to optimize the Algebraic Reconstruction Technique (ART), which reconstructs images from their projections, by varying the relaxation factor employed in the updating procedure. In some of the imaging situations studied, it is found that the optimization of constrained ART, in which a nonnegativity constraint is invoked, can vastly increase the detectability of objects. There is little improvement attained for unconstrained ART.
Keywords: task performance, evaluation of image-processing algorithms, algebraic reconstruction technique (ART), relaxation factor, reconstruction optimization, nonnegativity constraint, deletectability, Monte Carlo technique
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