NODE-ImgNet : a PDE-informed effective and robust model for image denoising
Xie, Xinheng and Wu, Yue and Ni, Hao and He, Cuiyu (2023) NODE-ImgNet : a PDE-informed effective and robust model for image denoising. Other. arXiv.org, Ithaca, New York. (https://doi.org/10.48550/arXiv.2305.11049)
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Abstract
Inspired by the traditional partial differential equation (PDE) approach for image denoising, we propose a novel neural network architecture, referred as NODE-ImgNet, that combines neural ordinary differential equations (NODEs) with convolutional neural network (CNN) blocks. NODE-ImgNet is intrinsically a PDE model, where the dynamic system is learned implicitly without the explicit specification of the PDE. This naturally circumvents the typical issues associated with introducing artifacts during the learning process. By invoking such a NODE structure, which can also be viewed as a continuous variant of a residual network (ResNet) and inherits its advantage in image denoising, our model achieves enhanced accuracy and parameter efficiency. In particular, our model exhibits consistent effectiveness in different scenarios, including denoising gray and color images perturbed by Gaussian noise, as well as real-noisy images, and demonstrates superiority in learning from small image datasets.
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Item type: Monograph(Other) ID code: 85816 Dates: DateEvent18 May 2023PublishedSubjects: Science > Mathematics Department: Faculty of Science > Mathematics and Statistics Depositing user: Pure Administrator Date deposited: 15 Jun 2023 14:30 Last modified: 11 Nov 2024 16:07 URI: https://strathprints.strath.ac.uk/id/eprint/85816