Sparsity-Based Poisson Denoising With Dictionary Learning

作者：Giryes Raja^*; Elad Michael

来源：IEEE Transactions on Image Processing, 2014, 23(12): 5057-5069.

DOI：10.1109/TIP.2014.2362057

• Denoising
• photon-limited imaging
• Poisson noise
• sparse representations
• signal modeling
• dictionary learning

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.

• 出版日期2014-12