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Projection data obtained through optical techniques for tomographic measurements, such as interferometry for refractive-index-based measurements, are often incomplete. This is due to limitations in the optical system, data storage, and alignment and vignette issues. Algebraic iterative reconstruction techniques are usually favored for such incomplete projections. A number of iterative algorithms, based on additive and multiplicative corrections, are used with a known simulated phantom and noise source to assess the reconstruction performance of incomplete data sets. In addition, we present reconstructions using experimental data obtained from a coherent gradient sensing interferometer for a steady temperature field in a fluid medium. We tested the algorithms using the simulated data set for incompleteness conditions similar to those found in the experimental data, and the best-performing algorithm is identified.

Three different approaches of generating reference images for projection moire interferometry are compared. An interpolation-based phase-shift technique that phase shifts the actual reference images is shown to reduce error by over 25% and to reduce processing time compared with the common cosinewave- and square-wave-based approaches. OCIS codes: 120.4120, 120.3180.