Root / CYBERNETICS AND PHYSICS / Volume 14, 2025, Number 4 / Parameter estimation using compressed sensing under unknown-but-bounded noise

Parameter estimation using compressed sensing under unknown-but-bounded noise

Irina Len, Victoria Erofeeva, Oleg Granichin, Vlada Smetanina

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.
CYBERNETICS AND PHYSICS, VOL. 14, NO. 4, 2025, 342–349
https://doi.org/10.35470/2226-4116-2025-14-4-342-349

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