Optimal Inversion of the Anscombe Transformation in Low-Count Poisson Image Denoising

By M. Mäkitalo, Alessandro Foi. IEEE Transactions on Image Processing, 2011

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Poisson Noise Reduction with Non-local PCA (2012, methodology): “In the case of 2D images we have compared with: – NLBayes [26] using Anscombe transform and the refined inverse transform proposed in [31].”
Image denoising: Can plain neural networks compete with BM3D? (2012, background): “Denoising algorithms which assume AWG noise might be applied to such images using some image transform [14].”
A High-Quality Denoising Dataset for Smartphone Cameras (2018, methodology): “For BM3D [10], we applied AnscombeBM3D [23] in raw-RGB space and CBM3D [9] in sRGB space.”
Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images (2021, methodology): “…as ours; 2) for BM3D, we use CBM3D with the parameter σ estimated by the method in [6] to denoise Gaussian noise, and use Anscombe transform [19] to denoise Poisson noise; 3) for DIP, N2V, and DBSN, we use the authors’ implementation, and for Noisier2Noise, we re-implement the authors’…”