Image denoising is about removing measurement noise from input image for better signal-to-noise ratio. In recent years, there has been great progress on the development of data-driven approaches for image denoising, which introduce various techniques and paradigms from machine learning in the design of image denoisers. This paper aims at investigating the application of ensemble learning in image denoising, which combines a set of simple base denoisers to form a more effective image denoiser. Based on different types of image priors, two types of base denoisers in the form of transform-shrinkage are proposed for constructing the ensemble. Then, with an effective re-sampling scheme, several ensemble-learning-based image denoisers are constructed using different sequential combinations of multiple proposed base denoisers. The experiments showed that sequential ensemble learning can effectively boost the performance of image denoising.

中文翻译：

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通过顺序集成学习进行图像去噪


图像去噪是指从输入图像中去除测量噪声，以获得更好的信噪比。近年来，数据驱动的图像去噪方法的发展取得了巨大进展，在图像去噪器的设计中引入了机器学习的各种技术和范例。本文旨在研究集成学习在图像去噪中的应用，它将一组简单的基础去噪器组合起来形成更有效的图像去噪器。基于不同类型的图像先验，提出了两种变换收缩形式的基础降噪器来构建集成。然后，通过有效的重采样方案，使用多个所提出的基本降噪器的不同顺序组合构建了几种基于集成学习的图像降噪器。实验表明，顺序集成学习可以有效提升图像去噪的性能。