IEEE Trans Image Process. 2014 Dec;23(12):5057-69. doi: 10.1109/TIP.2014.2362057. Epub 2014 Oct 8.
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.
泊松去噪问题出现在各种成像应用中，如微光摄影、医学成像和显微镜。在高信噪比（SNR）的情况下，有几种变换可以将泊松噪声转换为加性独立同分布的高斯噪声，对于后者有许多有效的算法。然而，在低 SNR 情况下，这些变换的准确性显著降低，需要一种直接依赖于真实噪声统计数据的策略。Salmon 等人采取了这种方法，提出了一种基于高斯混合模型的基于块的指数图像表示模型，从而获得了最先进的结果。在本文中，我们提出利用稀疏表示建模来对图像块进行处理，采用相同的指数思想。我们的方案在去噪过程中使用基于贪婪追踪和基于引导的停止条件的稀疏表示模型，并进行字典学习。所提出的方案的重建性能在高 SNR 情况下与领先方法竞争，在低 SNR 情况下达到最先进的结果。
