Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  reconstruct signals from underdetermined linear measurements where the componentwise gains of the measurement system are unknown a priori. The reconstruction is performed through an adaptation of the messagepassing algorithm called adaptive GAMP that enables joint gain calibration and signal estimation. To evaluate our approach,  apply it to the problem of sparse recovery and compare it against an l 1 -based approach.  numerically show that adaptive GAMP yields excellent results even for a moderate amount of data. It approaches the performance of oracle GAMP where the gains are perfectly known asymptotically.
['Ulugbek S. Kamilov', 'Aurelien Bourquard', 'Emrah Bostan', 'Michael Unser']
I am writing a research paper on a idea Autocalibrated signal reconstruction from linear measurements using adaptive GAMP
[{"from": "human", "value": "I am writing a research paper on a idea Autocalibrated signal reconstruction from linear measurements using adaptive GAMP"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  reconstruct signals from underdetermined linear measurements where the componentwise gains of the measurement system are unknown a priori. The reconstruction is performed through an adaptation of the messagepassing algorithm called adaptive GAMP that enables joint gain calibration and signal estimation. To evaluate our approach,  apply it to the problem of sparse recovery and compare it against an l 1 -based approach.  numerically show that adaptive GAMP yields excellent results even for a moderate amount of data. It approaches the performance of oracle GAMP where the gains are perfectly known asymptotically."}]