Fast total variation based image restoration under mixed Poisson-Gaussian noise model
| Autor: | Manu Ghulyani, Muthuvel Arigovindan |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Mixed model
Computer science Shot noise 010103 numerical & computational mathematics 02 engineering and technology Poisson distribution 01 natural sciences Regularization (mathematics) symbols.namesake Rate of convergence Gaussian noise Computer Science::Computer Vision and Pattern Recognition 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing 0101 mathematics Likelihood function Algorithm Electrical Engineering Image restoration |
| Zdroj: | ISBI |
| Popis: | Image acquisition in many biomedical imaging modalities is corrupted by Poisson noise followed by additive Gaussian noise. MLE based restoration methods that use the exact Likelihood function for this mixed model with non-quadratic regularization are very few. While it has been demonstrated that total variation (TV) based regularization methods give better results, such methods that use exact Poisson-Gaussian Likelihood are slow. Here, we propose an ADMM based fast algorithm for image restoration using exact Poisson-Gaussian Likelihood function and TV regularization. Specifically, we propose a novel variable splitting approach that enables isolating the complexity in the exact MLE functional from the image blurring operation, allowing a fast Newton-like iteration on the MLE functional. This leads to a significantly improved convergence rate of the overall ADMM iteration. The effectiveness of the proposed method is demonstrated using restoration examples. |
| Databáze: | OpenAIRE |
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