Comparative analysis shows that projection methods improve digital image reconstruction in remote sensing, suggesting effective techniques for sparse data.
A comparative analysis of sparse image reconstruction methods in remote sensing problems is presented. The efficiency of the methods is estimated by objective criteria of the quality of the reconstructed digital images. The following methods are used as tested: the interpolation method of the sequentially calculated Fourier spectrum, the projection method onto convex sets, the amplitude iteration method, and spline interpolation. The problem is the reconstruction of sparseness modeled according to a randomly uniform law (at least 90 percent of information is missing). The conducted study allows us to draw a conclusion about the fundamental possibility of using the proposed methods for reconstructing sparse images obtained during remote sensing.
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A. V. Kokoshkin (2025) studied this question.
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