Indirect Estimation of Signal-Dependent Noise With Nonadaptive Heterogeneous Samples
| Autor: | Alessandro Foi, Lucio Azzari |
|---|---|
| Přispěvatelé: | Tampere University, Research group: Signal and Image Restoration-RST, Department of Signal Processing, Signal Processing Research Community (SPRC) |
| Rok vydání: | 2014 |
| Předmět: |
Mathematical optimization
Gaussian Signal-To-Noise Ratio Sensitivity and Specificity Signal Image (mathematics) symbols.namesake Signal-to-noise ratio Image Interpretation Computer-Assisted Computer Simulation Mathematics Models Statistical Noise (signal processing) 213 Electronic automation and communications engineering electronics Reproducibility of Results Image Enhancement Computer Graphics and Computer-Aided Design Arbitrarily large Sample size determination Gaussian noise Data Interpretation Statistical Sample Size symbols Algorithm Algorithms Software |
| Zdroj: | IEEE Transactions on Image Processing. 23:3459-3467 |
| ISSN: | 1941-0042 1057-7149 |
| DOI: | 10.1109/tip.2014.2321504 |
| Popis: | We consider the estimation of signal-dependent noise from a single image. Unlike conventional algorithms that build a scatterplot of local mean-variance pairs from either small or adaptively selected homogeneous data samples, our proposed approach relies on arbitrarily large patches of heterogeneous data extracted at random from the image. We demonstrate the feasibility of our approach through an extensive theoretical analysis based on mixture of Gaussian distributions. A prototype algorithm is also developed in order to validate the approach on simulated data as well as on real camera raw images. |
| Databáze: | OpenAIRE |
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