English(EN) I-INR: Iterative Implicit Neural Representations

I-INR: 迭代隐式神经表示

作者 PulseAugur 编辑部 · [1 个来源] · 2026-04-29 04:00

研究人员推出迭代隐式神经表示（I-INRs），这是一个旨在增强现有隐式神经表示（INRs）的新框架。这种即插即用方法可迭代地改进信号重建，解决了标准INR在频谱偏差和噪声敏感性方面的局限性。I-INRs在参数和计算成本仅略微增加的情况下，实现了卓越的重建质量，在图像拟合和去噪等任务上优于WIRE、SIREN和Gauss等成熟方法。 AI

影响提高了信号处理和计算机视觉任务的重建质量和噪声鲁棒性。

排序理由这是一篇介绍改进现有方法的创新技术方法的学术论文。

在 arXiv cs.CV 阅读 →

I-INRs
WIRE

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arXiv cs.CV TIER_1 English(EN) · Ali Haider, Muhammad Salman Ali, Maryam Qamar, Tahir Khalil, Soo Ye Kim, Jihyong Oh, Enzo Tartaglione, Sung-Ho Bae · 2026-04-29 04:00

I-INR：迭代隐式神经表示

arXiv:2504.17364v4 Announce Type: replace Abstract: Implicit Neural Representations (INRs) have revolutionized signal processing and computer vision by modeling signals as continuous, differentiable functions parameterized by neural networks. However, INRs are prone to the spectr…

本文如何被排名

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I-INR：迭代隐式神经表示

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