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arXiv · 1803.00389

Poisson Image Denoising Using Best Linear Prediction: A Post-processing Framework

Milad Niknejad·Mario A. T. Figueiredo
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Abstract

In this paper, we address the problem of denoising images degraded by Poisson noise. We propose a new patch-based approach based on best linear prediction to estimate the underlying clean image. A simplified prediction formula is derived for Poisson observations, which requires the covariance matrix of the underlying clean patch. We use the assumption that similar patches in a neighborhood share the same covariance matrix, and we use off-the-shelf Poisson denoising methods in order to obtain an initial estimate of the covariance matrices. Our method can be seen as a post-processing step for Poisson denoising methods and the results show that it improves upon several Poisson denoising methods by relevant margins.

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Milad Niknejad, Mario A. T. Figueiredo. 2018-03-01. Poisson Image Denoising Using Best Linear Prediction: A Post-processing Framework. https://arxiv.org/abs/1803.00389

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