Poisson Image Denoising Using Best Linear Prediction: a Post-Processing Framework

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 these covariance matrices. Our method can be seen as a post-processing step for other Poisson denoising methods and the results show that it improves upon them by relevant margins.