Suppression of Correlated Noise with Similarity-based Unsupervised Deep Learning

• URL: http://arxiv.org/abs/2011.03384v6
• Date: Wed, 5 Jan 2022 18:47:20 GMT
• Title: Suppression of Correlated Noise with Similarity-based Unsupervised Deep Learning
• Authors: Chuang Niu, Mengzhou Li, Fenglei Fan, Weiwen Wu, Xiaodong Guo, Qing Lyu, and Ge Wang
• Abstract summary: Noise2Sim is an unsupervised deep denoising approach that works in a nonlocal nonlinear fashion to suppress correlated noises. Nosie2Sim recovers features from noisy low-dose and photon-counting CT images as effectively as or even better than supervised learning methods.
• Score: 7.61850613267116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image denoising is a prerequisite for downstream tasks in many fields. Low-dose and photon-counting computed tomography (CT) denoising can optimize diagnostic performance at minimized radiation dose. Supervised deep denoising methods are popular but require paired clean or noisy samples that are often unavailable in practice. Limited by the independent noise assumption, current unsupervised denoising methods cannot process correlated noises as in CT images. Here we propose the first-of-its-kind similarity-based unsupervised deep denoising approach, referred to as Noise2Sim, that works in a nonlocal and nonlinear fashion to suppress not only independent but also correlated noises. Theoretically, Noise2Sim is asymptotically equivalent to supervised learning methods under mild conditions. Experimentally, Nosie2Sim recovers intrinsic features from noisy low-dose CT and photon-counting CT images as effectively as or even better than supervised learning methods on practical datasets visually, quantitatively and statistically. Noise2Sim is a general unsupervised denoising approach and has great potential in diverse applications.

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