Accuracy of Model-based and Learning-based Approaches for Image Noise Variance Estimation

2020 ◽  
Author(s):  
Mykhail L. Uss ◽  
Vladimir V. Lukin ◽  
Benoit Vozel ◽  
Kacem Chehdi
Keyword(s):  
Variance Estimation ◽  
Image Noise ◽  
Noise Variance ◽  
Model Based

Maximum-Likelihood, Magnitude-Based, Amplitude and Noise Variance Estimation

2021 ◽  
pp. 1-1
Author(s):  
Ming-Wei Wu ◽  
Yan Jin ◽  
Yan Li ◽  
Tianyu Song ◽  
Pooi Yuen Kam
Keyword(s):  
Maximum Likelihood ◽  
Variance Estimation ◽  
Noise Variance

A note on design-based versus model-based variance estimation in stereology

2002 ◽  
Vol 34 (03) ◽  
pp. 484-490 ◽  
Author(s):  
Asger Hobolth ◽  
Eva B. Vedel Jensen
Keyword(s):  
Mathematical Model ◽  
Original Problem ◽  
Variance Estimation ◽  
Point Of View ◽  
Systematic Sampling ◽  
Variance Estimator ◽  
Shape Model ◽  
Model Based ◽  
Versus Model ◽  
Special Case

Recently, systematic sampling on the circle and the sphere has been studied by Gual-Arnau and Cruz-Orive (2000) from a design-based point of view. In this note, it is shown that their mathematical model for the covariogram is, in a model-based statistical setting, a special case of the p-order shape model suggested by Hobolth, Pedersen and Jensen (2000) and Hobolth, Kent and Dryden (2002) for planar objects without landmarks. Benefits of this observation include an alternative variance estimator, applicable in the original problem of systematic sampling. In a wider perspective, the paper contributes to the discussion concerning design-based versus model-based stereology.

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