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.
View on arXiv@article{haider2025_2504.17364,
title={ I-INR: Iterative Implicit Neural Representations },
author={ Ali Haider and Muhammad Salman Ali and Maryam Qamar and Tahir Khalil and Soo Ye Kim and Jihyong Oh and Enzo Tartaglione and Sung-Ho Bae },
journal={arXiv preprint arXiv:2504.17364},
year={ 2025 }
}