Structural Similarity Measure to Assess Improvement by Noise in Nonlinear Image Transmission

Rousseau, D.; Delahaies, Agnès; Chapeau‐Blondeau, François

doi:10.1109/lsp.2009.2031734

Cited by 17 publications

(12 citation statements)

References 22 publications

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“…New forms of useful-noise effect, related to stochastic resonance or not, continue to be demonstrated. One current domain of interest is the study of non-trivial transposition of stochastic resonance to image processing [2], [14]. In this report, we come back on the results of a prospective study presented in [9] and we establish a link between this work [9] and the mechanisms encountered in the monodimensional domain.…”

Section: Introductionmentioning

confidence: 70%

Noise-enhanced nonlinear PDE for edge restoration in scalar images

Histace

Rousseau

2010

2010 International Conference of Soft Computing and Pattern Recognition

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The report proposed an interpretation for the mechanism of noise-enhanced image restoration with nonlinear PDE (Partial Differential Equation) recently demonstrated in literature. A link is established between the action of noise in a nonlinear Perona-Malik anisotropic diffusion and stochastic resonance in memoryless nonlinear systems for 1-D signals.Index Terms-stochastic resonance, anisotropic diffusion, image restoration, nonlinear image processing.

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Section: Introductionmentioning

confidence: 70%

Noise-enhanced nonlinear PDE for edge restoration in scalar images

Histace

Rousseau

2010

2010 International Conference of Soft Computing and Pattern Recognition

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“…It has been shown to display a good match with visual perception of image similarity [32,33]. The ability of the SSIM to be in good match with the visual appreciation of image quality in a stochastic resonance context has recently been demonstrated in [34] with an additive signal-noise mixture. We now present the use of the SSIM in the context of the nonadditive signal-noise mixture of MRI.…”

Section: Noise-assisted Image Transmissionmentioning

confidence: 99%

Tuning the Noise in Magnetic Resonance Imaging to Maximize Nonlinear Information Transmission

et al. 2013

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We demonstrate a new instance of useful-noise effect or stochastic resonance, occurring in magnetic resonance imaging (MRI). Based on the physics of signal–noise coupling specific to MRI, we establish the possibility of regimes where nonlinear post-processing can benefit from an increase in the level of the noise present in the MRI apparatus. The validation is obtained by both theoretical analysis and experimental observations. We especially show that the beneficial tuning of the noise can be practically achieved by controlling the bandwidth of the sampling receiver of the MRI apparatus. These results constitute a nontrivial extension of stochastic resonance in the domain of images, arising here with a signal–noise coupling in MRI which is distinct from the purely additive or multiplicative couplings previously investigated in the framework of useful-noise effect.

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“…The mechanism of such amplification has been addressed theoretically and experimentally in past research [2,7,8]. The applicability of this interesting phenomenon to various fields, including system control [9], imaging [10,11], nano-devices [12][13][14], localization [15], and fault diagnosis [16,17], has also been discussed.…”

Section: Introductionmentioning

confidence: 99%

Enhancing a BPSK receiver by employing a practical parallel network with Stochastic resonance

Tadokoro

Tanaka

Nakashima

et al. 2019

NOLTA

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Stochastic resonance (SR) is a noise-enhancement phenomenon that enables the detection of sub-threshold signals by adding noise and using nonlinear systems. This paper explores the applicability of SR in a BPSK receiver with sub-threshold signals. Although received signals are amplified as a result of the nonlinear behavior of the receiver, they are somewhat distorted. This results in the lower performance of SR receivers in comparison with linear receivers. Employing a parallel network of SR systems is expected to solve this problem. The present theoretical analysis demonstrates that in a certain noise intensity range, the output of the network can fully describe an input sub-threshold signal, and hence, the performance close to that of the linear receivers can be obtained. The effectiveness of the SR receiver was also demonstrated through a numerical example of the bit error rate (BER). However, achieving good BER performance requires an infinite number of arrayed SR systems, which is not realistic in practical systems. A design framework for an SR network with a finite number of elements and an appropriate noise intensity that can realize BER performance close to that in linear systems is also provided.

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Structural Similarity Measure to Assess Improvement by Noise in Nonlinear Image Transmission

Cited by 17 publications

References 22 publications

Noise-enhanced nonlinear PDE for edge restoration in scalar images

Noise-enhanced nonlinear PDE for edge restoration in scalar images

Tuning the Noise in Magnetic Resonance Imaging to Maximize Nonlinear Information Transmission

Enhancing a BPSK receiver by employing a practical parallel network with Stochastic resonance

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