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Combinatorial design of near-optimum masks for coded aperture imaging

In coded aperture imaging the attainable quality of the reconstructed images strongly depends on the choice of the aperture pattern. Optimum mask patterns can be designed from binary arrays with constant sidelobes of their periodic autocorrelation function, the so-called URAs. However, URAs exist for a restricted number of aperture sizes and open fractions only. Using a mismatched filter decoding scheme, artifact-free reconstructions can be obtained even if the aperture array violates the URA condition. A general expression and an upper bound for the signal-to-noise ratio as a function of the aperture array and the relative detector noise level are derived. Combinatorial optimization algorithms, such as the great deluge algorithm, are employed for the design of near-optimum aperture arrays. The signal-to-noise ratio of the reconstructions is predicted to be only slightly inferior to the URA case while no restrictions with respect to the aperture size or open fraction are imposed.