Optimal Inversion of the Generalized Anscombe Transformation for Poisson-Gaussian Noise

By M. Mäkitalo, Alessandro Foi. IEEE Transactions on Image Processing, 2013

363 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

CycleISP: Real Image Restoration via Improved Data Synthesis (2020, methodology): “* denotes that these methods use variance stabilizing transform (VST) [40] dataset [31] and split them into a ratio of 90:5:5 for training, validation and testing.”
A Review Paper: Noise Models in Digital Image Processing (2015, methodology): “After all to get a noisy image ( , ) Z j k from the Poisson-Gaussian model is based on [22-23].”
Benchmarking Denoising Algorithms with Real Photographs (2017, methodology): “[34] and the closed-form approximation to its exact unbiased inverse [26].”
Plug-and-Play ADMM for Image Restoration: Fixed-Point Convergence and Applications (2016, methodology): “We are aware of other existing Poisson denoising methods such as [17], [37], [49]–[51].”
Decomposable Nonlocal Tensor Dictionary Learning for Multispectral Image Denoising (2014, methodology): “To remove the dependency of the noise variance on the underlying signal before the denoising and compensate the effects of the bias in the produced filtered estimate, in all experiments, the noisy MSI was firstly reformulated by a variance-stabilizing transformation (VST) [19] before implementing a ”