Optimal phase-space projection for noise reduction

Optimal phase-space projection for noise reduction.

In this communication we will reexamine the widely studied technique of phase-space projection. By imposing a time-domain constraint on the residual noise, we deduce a more general version of the optimal projector, which includes those appearing in previous literature as subcases but does not assume the independence between the clean signal and the noise. As an application, we will apply this t...

Generalized phase space projection for nonlinear noise reduction

Improved phase space projection methods, adapted from related work in the linear signal processing field based on subspace decomposition, are presented for application to the problem of additive noise reduction in the context of phase space analysis. These methods improve upon existing methods such as Broomhead–King singular spectrum analysis projection by minimizing overall signal distortion s...

Circular Mean Filtering For Textures Noise Reduction

In this paper, a special preprocessing operations (filter) is proposed to decrease the effects of noise of textures. This filter using average of circular neighbor points (Cmean) to reduce noise effect. Comparing this filter with other average filters such as square mean filter and square median filter indicates that it provides more noise reduction and increases the classification accuracy...

Smooth local subspace projection for nonlinear noise reduction.

Many nonlinear or chaotic time series exhibit an innate broad spectrum, which makes noise reduction difficult. Local projective noise reduction is one of the most effective tools. It is based on proper orthogonal decomposition (POD) and works for both map-like and continuously sampled time series. However, POD only looks at geometrical or topological properties of data and does not take into ac...

Optimal Time-Domain Noise Reduction Filters - ReadingSample