IEEE Trans Image Process - Sparsity-based Poisson denoising with dictionary learning.

Tópicos

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Resumo

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-independent identically distributed. 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. Salmon et al took this route, proposing a patch-based exponential image representation model based on 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.

Resumo Limpo

problem poisson denois appear various imag applic lowlight photographi medic imag microscopi case high snr sever transform exist convert poisson nois additiveindepend ident distribut gaussian nois mani effect algorithm avail howev lowsnr regim transform signific less accur strategi reli direct true nois statist requir salmon et al took rout propos patchbas exponenti imag represent model base gaussian mixtur model lead stateoftheart result paper propos har sparserepresent model imag patch adopt exponenti idea scheme use greedi pursuit bootstrappingbas stop condit dictionari learn within denois process reconstruct perform propos scheme competit lead method high snr achiev stateoftheart result case low snr

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