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Polarimetric SAR Speckle Filtering
Published in Jong-Sen Lee, Eric Pottier, Polarimetric Radar Imaging, 2017
Dealing with multiplicative noise is somewhat more complicated than dealing with additive noise. Researchers from digital image processing community prefer to use this homomorphic approach by converting the multiplicative noise into additive noise with logarithm. Arsenault and Levesque [31] were the first to propose such a technique, and then apply the additive local statistic filter developed by Lee [10] to filter speckle noise. This approach is being avoided in SAR remote sensing community for a number of reasons: introducing bias and blurring strong scatterers. This is because the process of taking logarithm, averaging the logarithmic values, and then taking inverse logarithm is not identical to averaging of pixel values directly. SAR has very high dynamic range, which will be logarithmically compressed. The strong signals are severely suppressed relative to the weak signals. The local mean and local variance computed in the logarithmic domain do not represent those in the original domain. The use of logarithm would suppress high returns much more than low returns.
Climate: Extreme Events
Published in Yeqiao Wang, Atmosphere and Climate, 2020
The non-Gaussian stochastic approach makes use of stochastic theory to evaluate extreme events and the physics that governs these events.[4] Assuming that weather and climate dynamics are split into a slow (i.e., slowly decorrelating) and a fast (i.e., rapidly decorrelating) contribution, weather and climate variability can be approximated by a stochastic system with a predictable deterministic component and an unpredictable noise component. In general, the deterministic part is non-linear and the stochastic part is state dependent. The stochastic approach takes advantage of the non-Gaussian structure of the PDF by linking a stochastic model to the observed non-Gaussianity. This can be done in two conceptually different ways. On the one hand, if the deterministic component is non-linear and the stochastic component is state independent, the non-Gaussianity is due to the non-linear deterministic part. On the other hand, if the deterministic component is linear and the stochastic component is state dependent, the non-Gaussianity is due to the state dependent noise. Of course, any combination of the two mechanisms is also possible. Although the nonlinear approach with state-independent noise captures some types of non-Gaussian climate variability well,[20,21] it recently became clear that state-dependent (or multiplicative) noise plays a major role in describing weather and climate extremes.141 The physical significance of multiplicative noise is that it has the potential to produce non-Gaussian statistics in linear systems. In particular, Sura and Sardeshmukh,[22] Sardeshmukh and Sura,[23] and Sura[4] attribute extreme anomalies to stochastically forced linear dynamics, where the strength of the stochastic forcing depends linearly on the flow itself (i.e., linear multiplicative noise). Most important, because the theory makes clear and testable predictions about non-Gaussian variability, it can be verified by analyzing the detailed non-Gaussian statistics of oceanic and atmospheric variability.
Noise and Interference
Published in Michel Daoud Yacoub, Foundations of Mobile Radio Engineering, 2019
Noise disturbance is said to be of the additive or multiplicative type, depending on whether the modification introduced to the signal is of additive or multiplicative nature, respectively. The additive noise is just superimposed on the signal whereas the multiplicative noise can be viewed as an amplitude modulation of the signal by the noise.
Application of Human Movement and Movement Scoring Technology in Computer Vision Feature in Sports Training
Published in IETE Journal of Research, 2021
The noise model can be divided into additive noise and multiplicative noise models. The output under the action of noise is defined as h(a,b), then the additive noise model can be expressed by the following formula The multiplicative noise model can be expressed by formula (8)