Noise measurement

About: Noise measurement is a research topic. Over the lifetime, 19776 publications have been published within this topic receiving 308180 citations.

Hybrid Noise Removal in Hyperspectral Imagery With a Spatial–Spectral Gradient Network

Abstract: The existence of hybrid noise in hyperspectral images (HSIs) severely degrades the data quality, reduces the interpretation accuracy of HSIs, and restricts the subsequent HSI applications. In this paper, the spatial–spectral gradient network (SSGN) is presented for mixed noise removal in HSIs. The proposed method employs a spatial–spectral gradient learning strategy, in consideration of the unique spatial structure directionality of sparse noise and spectral differences with additional complementary information for effectively extracting intrinsic and deep features of HSIs. Based on a fully cascaded multiscale convolutional network, SSGN can simultaneously deal with different types of noise in different HSIs or spectra by the use of the same model. The simulated and real-data experiments undertaken in this study confirmed that the proposed SSGN outperforms at mixed noise removal compared with the other state-of-the-art HSI denoising algorithms, in evaluation indices, visual assessments, and time consumption.

Near optimal detection of signals in impulsive noise modeled with a symmetric /spl alpha/-stable distribution

Abstract: There has been great interest in symmetric /spl alpha/-stable distributions which have proved to be very good models for impulsive noise. However, most of the classical non-Gaussian receiver design techniques cannot be extended to the symmetric /spl alpha/-stable noise case since these techniques require an explicit compact analytical form for the probability density function (PDF) of the noise distribution which /spl alpha/-stable distributions do not possess. A new analytical representation has been suggested for the symmetric /spl alpha/-stable PDF which is based on scale mixtures of Gaussians. Based on this new analytical representation, this paper introduces a novel near-optimal receiver for the detection of signals in symmetric /spl alpha/-stable noise. The performance of the new receiver is very close to the locally optimum receiver and is significantly better than the performance of previously suggested sub-optimum receivers. The new technique has important potential in radar, sonar, and other applications.

Oscillatory stability limit prediction using stochastic subspace identification

Abstract: Determining stability limits and maximum loading margins in a power system is important and can be of significant help for system operators for preventing stability problems In this paper, stochastic subspace identification is employed to extract the critical mode(s) from the measured ambient noise without requiring artificial disturbances (eg, line outages, generator tripping, and adding/removing loads), so that the identified critical mode may be used as an online index to predict the closest oscillatory instability The proposed index is not only independent of system models and truly represents the actual system, but it is also computationally efficient The application of the proposed index to several realistic test systems is examined using a transient stability program and PSCAD/EMTDC, which has detailed models that can capture the full dynamic response of the system The results show the feasibility of using the proposed identification technique and index for online detection of proximity to oscillatory stability problems

Procedural noise using sparse Gabor convolution

Abstract: Noise is an essential tool for texturing and modeling. Designing interesting textures with noise calls for accurate spectral control, since noise is best described in terms of spectral content. Texturing requires that noise can be easily mapped to a surface, while high-quality rendering requires anisotropic filtering. A noise function that is procedural and fast to evaluate offers several additional advantages. Unfortunately, no existing noise combines all of these properties.In this paper we introduce a noise based on sparse convolution and the Gabor kernel that enables all of these properties. Our noise offers accurate spectral control with intuitive parameters such as orientation, principal frequency and bandwidth. Our noise supports two-dimensional and solid noise, but we also introduce setup-free surface noise. This is a method for mapping noise onto a surface, complementary to solid noise, that maintains the appearance of the noise pattern along the object and does not require a texture parameterization. Our approach requires only a few bytes of storage, does not use discretely sampled data, and is nonperiodic. It supports anisotropy and anisotropic filtering. We demonstrate our noise using an interactive tool for noise design.

Robust order-based methods for feature description

Abstract: Feature-based methods have found increasing use in many applications such as object recognition, 3D reconstruction and mosaicing. In this paper, we focus on the problem of matching such features. While a histogram-of-gradients type methods such as SIFT, GLOH and Shape Context are currently popular, several papers have suggested using orders of pixels rather than raw intensities and shown improved results for some applications. The papers suggest two different techniques for doing so: (1) A Histogram of Relative Orders in the Patch and (2) A Histogram of LBP codes. While these methods have shown good performance, they neglect the fact that the orders can be quite noisy in the presence of Gaussian noise. In this paper, we propose changes to these approaches to make them robust to Gaussian noise. We also show how the descriptors can be matched using recently developed more advanced techniques to obtain better matching performance. Finally, we show that the two methods have complimentary strengths and that by combining the two descriptors, one obtains much better results than either of them considered separately. The results are shown on the standard 2D Oxford and the 3D Caltech datasets.