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

TL;DR

This paper introduces a post-processing framework for Poisson image denoising that leverages best linear prediction and patch similarity to enhance existing denoising methods, leading to improved results.

Contribution

It presents a novel patch-based post-processing approach using best linear prediction tailored for Poisson noise, improving denoising performance.

Findings

Significant improvement over existing Poisson denoising methods.

Effective use of covariance matrix estimation from similar patches.

Framework applicable as a post-processing step for various denoising algorithms.

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.