Class-specific Poisson denoising by patch-based importance sampling

TL;DR

This paper introduces a class-specific Poisson denoising method using patch-based importance sampling, which effectively improves image recovery in low SNR conditions by leveraging class-specific patch clustering and Monte Carlo techniques.

Contribution

The paper presents a novel Poisson denoising approach that combines class-specific patch clustering with importance sampling to enhance image restoration performance.

Findings

Outperforms existing methods at low SNR regimes

Uses importance sampling for MMSE estimation

Effective for class-specific image denoising

Abstract

In this paper, we address the problem of recovering images degraded by Poisson noise, where the image is known to belong to a specific class. In the proposed method, a dataset of clean patches from images of the class of interest is clustered using multivariate Gaussian distributions. In order to recover the noisy image, each noisy patch is assigned to one of these distributions, and the corresponding minimum mean squared error (MMSE) estimate is obtained. We propose to use a self-normalized importance sampling approach, which is a method of the Monte-Carlo family, for the both determining the most likely distribution and approximating the MMSE estimate of the clean patch. Experimental results shows that our proposed method outperforms other methods for Poisson denoising at a low SNR regime.