Abstract
Summary We present a data recovery scheme in Dreamlet (Drumbeat-Beamlet, which is a type of physical wavelet) domain based on the concept of compressive sensing. The data are randomly sampled with sparse samples missing 50% of the original data. Data recovery is done by a basis pursuit decomposition method based on l1-norm optimization to search for the most efficient representation (least residual with minimum coefficients). Numerical tests show that Dreamlet representation is more efficient (with less coefficients) and the data recovery process is faster (with fewer iterations) than the curvelet method.
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