Sparse signal recovery from modulo observations

Abstract We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by relatively new imaging mechanism called imaging, which can be used to extend dynamic range systems; variations this have also been studied under category phase unwrapping. Signal reconstruction in regime with challenging ill-posed problem, and existing methods cannot directly. In paper, we propose novel approach solving recovery sparsity constraints for special case folding limited two periods. show that given sufficient number measurements, our algorithm perfectly recovers underlying signal. provide experiments validating on toy image data demonstrate its promising performance.