I-INR: Iterative Implicit Neural Representations

• URL: http://arxiv.org/abs/2504.17364v1
• Date: Thu, 24 Apr 2025 08:27:22 GMT
• Title: I-INR: Iterative Implicit Neural Representations
• Authors: Ali Haider, Muhammad Salman Ali, Maryam Qamar, Tahir Khalil, Soo Ye Kim, Jihyong Oh, Enzo Tartaglione, Sung-Ho Bae,
• Abstract summary: Implicit Neural Representations (INRs) have revolutionized signal processing and computer vision by modeling signals as continuous, differentiable functions parameterized by neural networks.<n>We propose Iterative Implicit Neural Representations (I-INRs) a novel plug-and-play framework that enhances signal reconstruction through an iterative refinement process.
• Score: 21.060226382403506
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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