ABSTRACT Medical, satellite or microscopic images differ in the imaging techniques used, hence their underlying noise distribution also are different. Most of the restoration methods including regularization models make prior assumptions about the noise to perform an efficient restoration. Here we propose a system that estimates and classifies the noise into different distributions by extracting the relevant features. The system provides information about the noise distribution and then it gets directed into the restoration module where an appropriate regularization method (based on the non-local framework) has been employed to provide an efficient restoration of the data. We have effectively addressed the distortion due to data-dependent noise distributions such as Poisson and Gamma along with data uncorrelated Gaussian noise. The studies have shown a 97.7% accuracy in classifying noise in the test data. Moreover, the system also shows the capability to cater to other popular noise distributions such as Rayleigh, Chi, etc.

中文翻译：

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使用非局部正则化框架的噪声分类和自动恢复系统

摘要 医学、卫星或显微图像所使用的成像技术不同，因此它们的潜在噪声分布也不同。包括正则化模型在内的大多数恢复方法都对噪声进行了先验假设，以执行有效的恢复。在这里，我们提出了一个系统，通过提取相关特征来估计噪声并将其分类为不同的分布。该系统提供有关噪声分布的信息，然后将其引导到恢复模块中，在该模块中采用了适当的正则化方法（基于非局部框架）来提供数据的有效恢复。我们已经有效地解决了由数据相关噪声分布（例如泊松和伽玛）以及数据不相关的高斯噪声引起的失真。研究表明，对测试数据中的噪声进行分类的准确度为 97.7%。此外，该系统还显示出满足其他流行噪声分布的能力，如瑞利、Chi 等。