The Background Noise Estimation using Outer Product Expansion with Low-order Error

Abstract

This paper shows a new denoising algorithm based on the outer product expansion with lower norms for the background noise. We have proposed novel source separation methods using the outer product expansion with L₁ norm minimization. Effectiveness of outer product expansions for artificial signals and an electromagnetic wave data was represented. However, the denoising performance is decreasing with an increasing of local signals. In this paper, we propose the outer product expansion with lower norms (0.1 ∼ 0.9). Simulation results show that the proposed method produces the accurate background noise reduction.