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.

Enhancement and noise reduction of very low light level images

Abstract: A general method for image contrast enhancement and noise reduction is proposed in this paper. The method is developed especially for enhancing images acquired under very low light conditions where the features of images are nearly invisible and the noise is serious. By applying an improved and effective image de-haze algorithm to the inverted input image, the intensity can be amplified so that the dark areas become bright and the contrast get enhanced. Then, the joint-bilateral filter with the original green component as the edge image is introduced to suppress the noise. Experimental results validate the performance of the proposed approach.

Separating Signal from Noise Using Patch Recurrence across Scales

Abstract: Recurrence of small clean image patches across different scales of a natural image has been successfully used for solving ill-posed problems in clean images (e.g., super-resolution from a single image). In this paper we show how this multi-scale property can be extended to solve ill-posed problems under noisy conditions, such as image denoising. While clean patches are obscured by severe noise in the original scale of a noisy image, noise levels drop dramatically at coarser image scales. This allows for the unknown hidden clean patches to "naturally emerge" in some coarser scale of the noisy image. We further show that patch recurrence across scales is strengthened when using directional pyramids (that blur and sub sample only in one direction). Our statistical experiments show that for almost any noisy image patch (more than 99%), there exists a "good" clean version of itself at the same relative image coordinates in some coarser scale of the image. This is a strong phenomenon of noise-contaminated natural images, which can serve as a strong prior for separating the signal from the noise. Finally, incorporating this multi-scale prior into a simple denoising algorithm yields state-of-the-art denoising results.

Pseudo-Random High-Frequency Square-Wave Voltage Injection Based Sensorless Control of IPMSM Drives for Audible Noise Reduction

Abstract: High frequency (HF) signal injection is an effective sensorless control scheme for interior permanent-magnet synchronous motor (IPMSM) drives to achieve low and zero speed operation. However, the audible noise produced by the injected HF signal is often very shrill and harsh to hear, which restricts the actual application. In order to reduce the audible noise, a novel pseudo-random HF square-wave voltage injection scheme is proposed in this paper. The HF voltages with two different frequencies are randomly injected into the estimated rotor reference frame cycle by cycle, and a corresponding signal demodulation method for extracting the rotor position information is presented. Based on the principle analysis of this random frequency injection scheme, the digital time-delay effect in HF signal is considered and a compensation method for signal demodulation is proposed, which is effective in reducing position estimation error. Then, the power spectra density (PSD) in fixed frequency and pseudo-random frequency injection schemes are compared both theoretically and experimentally. The distribution of HF voltage and current PSD is extended by using the proposed injection scheme. Finally, this sensorless control method is verified by simulation and experiment on a 2.2-kW IPMSM drive platform.

Building robust wavelet estimators for multicomponent images using Stein's principle

Abstract: Multichannel imaging systems provide several observations of the same scene which are often corrupted by noise. In this paper, we are interested in multispectral image denoising in the wavelet domain. We adopt a multivariate statistical approach in order to exploit the correlations existing between the different spectral components. Our main contribution is the application of Stein's principle to build a new estimator for arbitrary multichannel images embedded in additive Gaussian noise. Simulation tests carried out on optical satellite images show that the proposed method outperforms conventional wavelet shrinkage techniques.

An Analysis of a Real-Time Transform Domain Filtering Digital Communication System--Part I: Narrow-Band Interference Rejection

Abstract: This paper provides a detailed analysis of the performance of systems recently proposed by various authors in which the received signal is Fourier transformed in real time (usually with a surface acoustic wave device) and then filtered by a multiplication of the transformed signal by an appropriate transfer function. We shall refer to this operation as transform domain filtering. General expressions for the output waveforms of the system will be derived, and a specific example illustrating the performance of the system when operating in the presence of a narrow-band interferer plus additive white Gaussian noise will be presented. It will be shown that certain filtering techniques not feasible in conventional systems, such as filtering with ideal bandpass filters, become straightforward using transform domain filtering. Finally, a receiver will be described which will eliminate that intersymbol interference between adjacent data symbols due to filtering at the receiver.