Parameter estimation using compressed sensing under unknown-but-bounded noise
Autors:
Irina Len , Victoria Erofeeva , Граничин Олег Николаевич , Vlada Smetanina ,
Pages:
342–349
Annotation:
Standard compressed sensing (CS) theory typically assumes that noise is bounded in ℓ2 -norm (e.g., Gaussian). In practice, noise can be unknown-but-bounded (for example, in low-light imaging or MRI artifacts). In this work a new CS recovery algorithm for parameter estimation under unknown-but-bounded noise is proposed. Experiments on images with various non-Gaussian noises demonstrate that proposed method outperforms classical ℓ2 -constrained recovery.
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