Cross-Frequency Implicit Neural Representation with Self-Evolving Parameters

• URL: http://arxiv.org/abs/2504.10929v1
• Date: Tue, 15 Apr 2025 07:14:35 GMT
• Title: Cross-Frequency Implicit Neural Representation with Self-Evolving Parameters
• Authors: Chang Yu, Yisi Luo, Kai Ye, Xile Zhao, Deyu Meng,
• Abstract summary: Implicit neural representation (INR) has emerged as a powerful paradigm for visual data representation.<n>We propose a self-evolving cross-frequency INR using the Haar wavelet transform (termed CF-INR), which decouples data into four frequency components and employs INRs in the wavelet space.<n>We evaluate CF-INR on a variety of visual data representation and recovery tasks, including image regression, inpainting, denoising, and cloud removal.
• Score: 52.574661274784916
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Implicit neural representation (INR) has emerged as a powerful paradigm for visual data representation. However, classical INR methods represent data in the original space mixed with different frequency components, and several feature encoding parameters (e.g., the frequency parameter $\omega$ or the rank $R$) need manual configurations. In this work, we propose a self-evolving cross-frequency INR using the Haar wavelet transform (termed CF-INR), which decouples data into four frequency components and employs INRs in the wavelet space. CF-INR allows the characterization of different frequency components separately, thus enabling higher accuracy for data representation. To more precisely characterize cross-frequency components, we propose a cross-frequency tensor decomposition paradigm for CF-INR with self-evolving parameters, which automatically updates the rank parameter $R$ and the frequency parameter $\omega$ for each frequency component through self-evolving optimization. This self-evolution paradigm eliminates the laborious manual tuning of these parameters, and learns a customized cross-frequency feature encoding configuration for each dataset. We evaluate CF-INR on a variety of visual data representation and recovery tasks, including image regression, inpainting, denoising, and cloud removal. Extensive experiments demonstrate that CF-INR outperforms state-of-the-art methods in each case.

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