Abstract

We present a new algorithm for image restoration in limited-angle chromotomography. The algorithm is a generalization of the technique considered previously by the authors, based on a hybrid of a direct method of inversion and the iterative method of projections onto convex sets. The generalization is achieved by introducing a new object domain constraint. This constraint takes advantage of hyperspectral data redundancy and is realized by truncating the singular-value decomposition of the spatial–chromatic image matrix. As previously, the transform domain constraint is defined in terms of nonzero singular values of the system transfer function matrix. The new algorithm delivers high image fidelity, converges rapidly, and is easy to implement. Results of experiments on real data are included.

© 1999 Optical Society of America

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