Calibrated Harmonic Overlaid Implicit Neural Representations for Multi-Dimensional Data

Implicit neural representation (INR) has emerged as a pow-erful prior for multi-dimensional data (e.g., multispectral images andvideos). However, most INR methods employing periodic activation func-tions (e.g., Sine) predominantly rely on function composition. This mech-anism introduces optimization instability as network depth increases,thereby limiting their performance. Meanwhile, these methods fail to in-corporate proper physical priors to effectively alleviate spectrum bias. Toaddress these issues, inspired by the commonalities between deep periodicnetworks and generalized Fourier series, we propose a novel CalibratedHarmonic Overlaid Implicit Neural Representation (CHOIR). Specifi-cally, we utilize Coordinated Harmonic Superposition (CHS) to replacethe conventional function composition used in most INRs, thereby en-suring optimization stability when scaling network depth. Furthermore,we introduce a Perceptual Spectrum Calibration (PSC) to mitigate spec-trum bias. This calibration embeds the ubiquitous power-law spectrumprior of natural images and adjusts the globally fixed spectrum towards aphysically plausible log-uniform distribution. Extensive experiments onvarious multidimensional data recovery problems demonstrate that ourmethod achieves superior performance over state-of-the-art approaches.Code is available at https://github.com/chorl0229/CHOIR.