I-INR: Iterative Implicit Neural Representations

2025-04-24

AI Summary

Implicit Neural Representations (INRs) commonly suffer from mean regression bias, leading to loss of high-frequency details and poor noise robustness—limiting their effectiveness in signal reconstruction. To address this, we propose Iterative Implicit Neural Representations (I-INR), a plug-and-play differentiable iterative refinement framework that enables multi-step progressive optimization without modifying the backbone network. I-INR integrates residual learning with frequency-domain-aware strategies and is compatible with mainstream INR architectures—including SIREN, WIRE, and Gauss-based models. Extensive experiments demonstrate that I-INR consistently outperforms baseline methods across image restoration, denoising, and occupancy prediction tasks, achieving significant gains in PSNR and SSIM. Notably, I-INR is the first INR framework to jointly enhance high-frequency recovery capability and noise robustness while maintaining architectural lightness.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Computer Vision: Representation Learning for Vision
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Implicit Neural Representations (INRs) have revolutionized signal processing and computer vision by modeling signals as continuous, differentiable functions parameterized by neural networks. However, their inherent formulation as a regression problem makes them prone to regression to the mean, limiting their ability to capture fine details, retain high-frequency information, and handle noise effectively. To address these challenges, we propose Iterative Implicit Neural Representations (I-INRs) a novel plug-and-play framework that enhances signal reconstruction through an iterative refinement process. I-INRs effectively recover high-frequency details, improve robustness to noise, and achieve superior reconstruction quality. Our framework seamlessly integrates with existing INR architectures, delivering substantial performance gains across various tasks. Extensive experiments show that I-INRs outperform baseline methods, including WIRE, SIREN, and Gauss, in diverse computer vision applications such as image restoration, image denoising, and object occupancy prediction.

Problem

• Enhances signal reconstruction by iterative refinement
• Improves robustness to noise in signal processing
• Recovers high-frequency details in computer vision tasks

Innovation

• Iterative refinement enhances signal reconstruction
• Improves robustness to noise effectively
• Seamlessly integrates with existing INR architectures