Class-specific Poisson denoising by patch-based importance sampling
This work addresses image denoising for specific domains, but it is incremental as it builds on existing patch-based and Monte Carlo techniques.
The paper tackles Poisson noise removal in images from a known class by using patch-based clustering and importance sampling, achieving superior performance compared to other methods at low signal-to-noise ratios.
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.