Abstract
We propose a method for compressive sensing and recovery of binary images. To achieve this, we combine two ideas: in the sensing step, ordered aperture patterns are employed instead of random aperture patterns, and in the recovery step, a dense reconstruction scheme replaces sparse reconstruction. We demonstrate that this approach is more effective for binary images than the state-of-the-art algorithms relying on random sensing and sparse reconstruction.
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