通过迭代优化提升隐式神经表示的高频细节与抗噪能力
I-INR: Iterative Implicit Neural Representations
- 设计可插拔的迭代重构框架，逐步恢复信号高频成分
- 在图像重建等任务中最高提升2.0 PSNR，仅增0.5%-2%参数
- 适合需要高精度重建与鲁棒性的视觉任务开发者使用
隐式神经表示（INRs）通过神经网络将信号建模为连续可微函数，革新了信号处理与计算机视觉。然而，INRs易受频谱偏差影响，难以保留高频信息，且抗噪能力弱。受迭代优化趋势启发，本文提出迭代隐式神经表示（I-INRs）。该可插拔框架通过迭代重构逐步恢复高频细节，提升噪声鲁棒性与泛化能力，显著改善重建质量。I-INRs可无缝集成至现有INR架构，仅增加0.5%-2%参数量；重构时额外消耗0.8%-1.6% FLOPs，却带来最高达+2.0 PSNR的性能提升。大量实验表明，I-INRs在图像拟合、图像去噪及物体占据预测等任务中均优于WIRE、SIREN和Gauss。代码已开源。
原文摘要 · Abstract (English)
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 spectral bias problem, limiting their ability to retain high-frequency information, and often struggle with noise robustness. Motivated by recent trends in iterative refinement processes, we propose Iterative Implicit Neural Representations (I-INRs). This novel plug-and-play framework iteratively refines signal reconstructions to restore high-frequency details, improve noise robustness, and enhance generalization, ultimately delivering superior reconstruction quality. I-INRs integrate seamlessly into existing INR architectures with only a 0.5-2% increase in parameters. During reconstruction, the iterative refinement adds just 0.8-1.6% additional FLOPs over the baseline while delivering a substantial performance boost of up to +2.0 PSNR. Extensive experiments demonstrate that I-INRs consistently outperform WIRE, SIREN, and Gauss across various computer vision tasks, including image fitting, image denoising, and object occupancy prediction. The code is available at github.com/optimizer077/I-INR.
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