Noise measurement

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

Learning to Restore Low-Light Images via Decomposition-and-Enhancement

Abstract: Low-light images typically suffer from two problems. First, they have low visibility (i.e., small pixel values). Second, noise becomes significant and disrupts the image content, due to low signal-to-noise ratio. Most existing lowlight image enhancement methods, however, learn from noise-negligible datasets. They rely on users having good photographic skills in taking images with low noise. Unfortunately, this is not the case for majority of the low-light images. While concurrently enhancing a low-light image and removing its noise is ill-posed, we observe that noise exhibits different levels of contrast in different frequency layers, and it is much easier to detect noise in the lowfrequency layer than in the high one. Inspired by this observation, we propose a frequency-based decompositionand- enhancement model for low-light image enhancement. Based on this model, we present a novel network that first learns to recover image objects in the low-frequency layer and then enhances high-frequency details based on the recovered image objects. In addition, we have prepared a new low-light image dataset with real noise to facilitate learning. Finally, we have conducted extensive experiments to show that the proposed method outperforms state-of-the-art approaches in enhancing practical noisy low-light images.

Estimation of perceptual entropy using noise masking criteria

Abstract: The perceptual entropy of each short-term section of the audio stimuli is estimated as the number of bits required to encode the short-term spectrum of the signal to the resolution measured by this process provide an entropy estimate, for transparent coding, of 1.4 (mean) or 2.1 (peak) bits/sample for telephone speech (200-3200-Hz bandwidth sampled at 8 kHz). The entropy measures for audio signals of other bandwidths and sampling rates is also reported. >

A Prony method for noisy data: Choosing the signal components and selecting the order in exponential signal models

Abstract: Prony's method is a simple procedure for determining the values of parameters of a linear combination of exponential functions. Until recently, even the modern variants of this method have performed poorly in the presence of noise. We have discovered improvements to Prony's method which are based on low-rank approximations to data matrices or estimated correlation matrices [6]-[8], [15]-[27], [34]. Here we present a different, often simpler procedure for estimation of the signal parameters in the presence of noise. This procedure has received only limited dissemination [35]. It is very close in form and assumptions to Prony's method. However, in preliminary tests, the performance of the method is close to that of the best available, more complicated, approaches which are based on maximum likelihood or on the use of eigenvector or singular value decompositions.

A single-letter characterization of optimal noisy compressed sensing

Abstract: Compressed sensing deals with the reconstruction of a high-dimensional signal from far fewer linear measurements, where the signal is known to admit a sparse representation in a certain linear space. The asymptotic scaling of the number of measurements needed for reconstruction as the dimension of the signal increases has been studied extensively. This work takes a fundamental perspective on the problem of inferring about individual elements of the sparse signal given the measurements, where the dimensions of the system become increasingly large. Using the replica method, the outcome of inferring about any fixed collection of signal elements is shown to be asymptotically decoupled, i.e., those elements become independent conditioned on the measurements. Furthermore, the problem of inferring about each signal element admits a single-letter characterization in the sense that the posterior distribution of the element, which is a sufficient statistic, becomes asymptotically identical to the posterior of inferring about the same element in scalar Gaussian noise. The result leads to simple characterization of all other elemental metrics of the compressed sensing problem, such as the mean squared error and the error probability for reconstructing the support set of the sparse signal. Finally, the single-letter characterization is rigorously justified in the special case of sparse measurement matrices where belief propagation becomes asymptotically optimal.

Threshold Detection in Narrow-Band Non-Gaussian Noise

Abstract: The Middleton Class A narrow-band non-Gaussian noise model [9]-[12] is examined. It is shown that this noise model (which is known to fit closely a variety of non-Gaussian noises) can itself be closely approximated by a computationally much simpler noise model. It is then shown by numerical examples that, for the problem of locally optimum detection, the simplest form of this approximation yields nearly optimal (asymptotic) performance. The performance of other simple suboptimal threshold detectors in Class A noise is also examined. Finally, a useful relationship between the Class A model and the e-mixture model is developed.