[2603.01028] Content-Aware Frequency Encoding for Implicit Neural Representations with Fourier-Chebyshev Features

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.01028 (cs) [Submitted on 1 Mar 2026] Title:Content-Aware Frequency Encoding for Implicit Neural Representations with Fourier-Chebyshev Features Authors:Junbo Ke, Yangyang Xu, You-Wei Wen, Chao Wang View a PDF of the paper titled Content-Aware Frequency Encoding for Implicit Neural Representations with Fourier-Chebyshev Features, by Junbo Ke and 3 other authors View PDF HTML (experimental) Abstract:Implicit Neural Representations (INRs) have emerged as a powerful paradigm for various signal processing tasks, but their inherent spectral bias limits the ability to capture high-frequency details. Existing methods partially mitigate this issue by using Fourier-based features, which usually rely on fixed frequency bases. This forces multi-layer perceptrons (MLPs) to inefficiently compose the required frequencies, thereby constraining their representational capacity. To address this limitation, we propose Content-Aware Frequency Encoding (CAFE), which builds upon Fourier features through multiple parallel linear layers combined via a Hadamard product. CAFE can explicitly and efficiently synthesize a broader range of frequency bases, while the learned weights enable the selection of task-relevant frequencies. Furthermore, we extend this framework to CAFE+, which incorporates Chebyshev features as a complementary component to Fourier bases. This combination provides a stronger and more stable frequency representa...