Sparsity Based Poisson Denoising with Dictionary Learning
This addresses denoising for imaging applications like low-light photography and medical imaging, but it is incremental as it builds on prior work by adapting sparse modeling.
The paper tackled Poisson denoising in imaging applications, particularly in low SNR regimes where existing methods are less accurate, by proposing a sparse-representation modeling approach with dictionary learning, achieving state-of-the-art results in low SNR cases.
The problem of Poisson denoising appears in various imaging applications, such as low-light photography, medical imaging and microscopy. In cases of high SNR, several transformations exist so as to convert the Poisson noise into an additive i.i.d. Gaussian noise, for which many effective algorithms are available. However, in a low SNR regime, these transformations are significantly less accurate, and a strategy that relies directly on the true noise statistics is required. A recent work by Salmon et al. took this route, proposing a patch-based exponential image representation model based on GMM (Gaussian mixture model), leading to state-of-the-art results. In this paper, we propose to harness sparse-representation modeling to the image patches, adopting the same exponential idea. Our scheme uses a greedy pursuit with boot-strapping based stopping condition and dictionary learning within the denoising process. The reconstruction performance of the proposed scheme is competitive with leading methods in high SNR, and achieving state-of-the-art results in cases of low SNR.