Are human observers performing wavelet denoising?

Denoising using wavelet

In many domains, one needs image analysis to access several useful data. As an astrophysician looks at spectral image to recover space data, as a doctor identifies tissues with ultrasounds, scientists needs high quality images to work with. Unfortunately, in fields like medicine or astronomy, we often have perturbations on images taken from bodies or from space. Phenomena like reflections creat...

Robust wavelet denoising

For extracting a signal from noisy data, waveshrink and basis pursuit are powerful tools both from an empirical and asymptotic point of view. They are especially efficient at estimating spatially inhomogeneous signals when the noise is Gaussian. Their performance is altered when the noise has a long tail distribution, for instance, when outliers are present. We propose a robust wavelet-based es...

Denoising via Recursive Wavelet

Non-Homogeneous Wavelet Denoising

Introduction Wavelet denoising has been widely used in signal processing to remove noise while preserving edges and spikes in the data, with minimal smoothing. A standard approach is wavelet shrinkage (WS) of the wavelet coefficients, dj, j = 1,...,n, [1,2] using a threshold t =σ 2 log n , a decision theoretic result based on risk minimization. Another approach is to treat thresholding as a hyp...

Can Wavelet Denoising Improve Motor Unit Potential Template Estimation?

Background: Electromyographic (EMG) signals obtained from a contracted muscle contain valuable information on its activity and health status. Much of this information lies in motor unit potentials (MUPs) of its motor units (MUs), collected during the muscle contraction. Hence, accurate estimation of a MUP template for each MU is crucial. Objective: To investigate the possibility of improv...