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.

Analysis and comparison of several simple impulsive noise mitigation schemes for OFDM receivers

Abstract: In this paper, we analyze and compare the performance of OFDM receivers with blanking, clipping and combined blanking-clipping nonlinear preprocessors in the presence of impulsive noise. Closed-form analytical expressions for the signal-to-noise ratio at the output of three types of nonlinearity are derived. Simulation results are provided that show good agreement with theory.

Power supply noise analysis methodology for deep-submicron VLSI chip design

Abstract: This paper describes a new design methodology to analyzethe on-chip power supply noise for high-performance microprocessors.Based on an integrated package-level andchip-level power bus model, and a simulated switching circuitmodel for each functional block, this methodology offersthe most complete and accurate analysis of Vdd distributionfor the entire chip. The analysis results not only providedesigners with the inductive ΔI noise and the resistive IRdrop data at the same time, but also allow designers to easilyidentify the hot spots on the chip and ΔV across the chip.Global and local optimization such as buffer sizing, powerbus sizing, and on-chip decoupling capacitor placement canthen be conducted to maximize the circuit performance andminimize the noise.

Kickback noise reduction techniques for CMOS latched comparators

Abstract: The latched comparator is a building block of virtually all analog-to-digital converter architectures. It uses a positive feedback mechanism to regenerate the analog input signal into a full-scale digital level. The large voltage variations in the internal nodes are coupled to the input, disturbing the input voltage-this is usually called kickback noise. This brief reviews existing solutions to minimize the kickback noise and proposes two new ones. HSPICE simulations of comparators implemented in a 0.18-/spl mu/m technology demonstrate their effectiveness.

Adaptive Volterra filters for active control of nonlinear noise processes

Abstract: This paper presents a Volterra filtered-X least mean square (LMS) algorithm for feedforward active noise control. The research has demonstrated that linear active noise control (ANC) systems can be successfully applied to reduce the broadband noise and narrowband noise, specifically, such linear ANC systems are very efficient in reduction of low-frequency noise. However, in some situations, the noise that comes from a dynamic system may he a nonlinear and deterministic noise process rather than a stochastic, white, or tonal noise process, and the primary noise at the canceling point may exhibit nonlinear distortion. Furthermore, the secondary path estimate in the ANC system, which denotes the transfer function between the secondary source (secondary speaker) and the error microphone, may have nonminimum phase, and hence, the causality constraint is violated. If such situations exist, the linear ANC system will suffer performance degradation. An implementation of a Volterra filtered-X LMS (VFXLMS) algorithm based on a multichannel structure is described for feedforward active noise control. Numerical simulation results show that the developed algorithm achieves performance improvement over the standard filtered-X LMS algorithm for the following two situations: (1) the reference noise is a nonlinear noise process, and at the same time, the secondary path estimate is of nonminimum phase; (2) the primary path exhibits the nonlinear behavior. In addition, the developed VFXLMS algorithm can also be employed as an alternative in the case where the standard filtered-X LMS algorithm does not perform well.

Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems

Abstract: While variational methods have been among the most powerful tools for solving linear inverse problems in imaging, deep (convolutional) neural networks have recently taken the lead in many challenging benchmarks. A remaining drawback of deep learning approaches is their requirement for an expensive retraining whenever the specific problem, the noise level, noise type, or desired measure of fidelity changes. On the contrary, variational methods have a plug-and-play nature as they usually consist of separate data fidelity and regularization terms. In this paper we study the possibility of replacing the proximal operator of the regularization used in many convex energy minimization algorithms by a denoising neural network. The latter therefore serves as an implicit natural image prior, while the data term can still be chosen independently. Using a fixed denoising neural network in exemplary problems of image deconvolution with different blur kernels and image demosaicking, we obtain state-of-the-art reconstruction results. These indicate the high generalizability of our approach and a reduction of the need for problemspecific training. Additionally, we discuss novel results on the analysis of possible optimization algorithms to incorporate the network into, as well as the choices of algorithm parameters and their relation to the noise level the neural network is trained on.