Poisson Image Denoising Using Best Linear Prediction: A Post-processing Framework
This work addresses image denoising for applications like microscopy or astronomy where Poisson noise is common, but it is incremental as it builds on existing methods as a post-processing framework.
The paper tackles the problem of denoising images degraded by Poisson noise by proposing a patch-based approach using best linear prediction as a post-processing step, which improves upon several existing Poisson denoising methods by relevant margins.
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.