Noise reduction

About: Noise reduction is a research topic. Over the lifetime, 25121 publications have been published within this topic receiving 300815 citations. The topic is also known as: denoising & noise removal.

Low-Frequency Noise Phenomena in Switched MOSFETs

Abstract: In small-area MOSFETs widely used in analog and RF circuit design, low-frequency (LF) noise behavior is increasingly dominated by single-electron effects. In this paper, the authors review the limitations of current compact noise models which do not model such single-electron effects. The authors present measurement results that illustrate typical LF noise behavior in small-area MOSFETs, and a model based on Shockley-Read-Hall statistics to explain the behavior. Finally, the authors treat practical examples that illustrate the relevance of these effects to analog circuit design. To the analog circuit designer, awareness of these single-electron noise phenomena is crucial if optimal circuits are to be designed, especially since the effects can aid in low-noise circuit design if used properly, while they may be detrimental to performance if inadvertently applied.

Adaptive Fractional-order Multi-scale Method for Image Denoising

Abstract: The total variation model proposed by Rudin, Osher, and Fatemi performs very well for removing noise while preserving edges. However, it favors a piecewise constant solution in BV space which often leads to the staircase effect, and small details such as textures are often filtered out with noise in the process of denoising. In this paper, we propose a fractional-order multi-scale variational model which can better preserve the textural information and eliminate the staircase effect. This is accomplished by replacing the first-order derivative with the fractional-order derivative in the regularization term, and substituting a kind of multi-scale norm in negative Sobolev space for the L 2 norm in the fidelity term of the ROF model. To improve the results, we propose an adaptive parameter selection method for the proposed model by using the local variance measures and the wavelet based estimation of the singularity. Using the operator splitting technique, we develop a simple alternating projection algorithm to solve the new model. Numerical results show that our method can not only remove noise and eliminate the staircase effect efficiently in the non-textured region, but also preserve the small details such as textures well in the textured region. It is for this reason that our adaptive method can improve the result both visually and in terms of the peak signal to noise ratio efficiently.

Poisson noise reduction with non-local PCA

Abstract: Photon limitations arise in spectral imaging, nuclear medicine, astronomy and night vision. The Poisson distribution used to model this noise has variance equal to its mean so blind application of standard noise removals methods yields significant artifacts. Recently, overcomplete dictionaries combined with sparse learning techniques have become extremely popular in image reconstruction. The aim of the present work is to demonstrate that for the task of image denoising, nearly state-of-the-art results can be achieved using small dictionaries only, provided that they are learned directly from the noisy image. To this end, we introduce patch-based denoising algorithms which perform an adaptation of PCA (Principal Component Analysis) for Poisson noise. We carry out a comprehensive empirical evaluation of the performance of our algorithms in terms of accuracy when the photon count is really low. The results reveal that, despite its simplicity, PCA-flavored denoising appears to be competitive with other state-of-the-art denoising algorithms.

Reduction of Guided Acoustic Wave Brillouin Scattering in Photonic Crystal Fibers

Abstract: Guided acoustic wave Brillouin scattering (GAWBS) generates phase and polarization noise of light propagating in glass fibers. This excess noise affects the performance of various experiments operating at the quantum noise limit. We experimentally demonstrate the reduction of GAWBS noise in a photonic crystal fiber in a broad frequency range by tailoring the acoustic modes using the photonic also as a phononic crystal. We compare the noise spectrum to the one of a standard fiber and observe a tenfold noise reduction in the frequency range up to 200 MHz. Based on our measurement results as well as on numerical simulations, we establish a model for the reduction of GAWBS noise in photonic crystal fibers.