发表机构
Institute of Computational Imaging, Beijing Information Science and Technology University; College of Computer Science (College of Software), Inner Mongolia University; Research Center for Spatiotemporal Intelligence, Inner Mongolia University（北京信息科技大学计算成像研究所; 内蒙古大学计算机学院（软件学院）; 内蒙古大学时空智能研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对隐式神经表示的频谱偏差问题，提出MLP与KAN分支融合的空间频率感知框架，结合小波变换实现互补频率建模，在多类信号上提升了重建保真度。
AI 中文摘要
隐式神经表示（INRs）已成为建模多维信号的极具吸引力的范式，它将连续坐标映射到信号值。然而，基于多层感知机（MLP）的INRs固有地存在频谱偏差，即偏向低频分量，抑制了对关键高频细节的重建。尽管傅里叶特征映射等现有技术可缓解该问题，但它们通常依赖敏感的手动调参，且易出现频谱伪影。本文提出一种空间频率感知的INRs框架，结合MLP分支与Kolmogorov-Arnold网络（KAN）分支以实现面向频率的互补建模：MLP分支提供面向低频的平滑结构表示，KAN分支补充局部变化与精细细节。为协调两个分支，我们将离散小波变换（DWT）和逆离散小波变换（IDWT）集成到输出融合阶段，将两个分支的输出分解为小波系数，对应系数相加融合后再进行逆小波重建。此外，小波域带分离正则化进一步惩罚MLP分支的高频响应和KAN分支的低频响应，从而鼓励面向频率的互补行为。在1D信号、2D图像、3D体积数据、符号距离函数、视频及4D光场上的实验表明，所提表示适用于所有评估的信号模态，且在各评估信号模态上均实现了更高的重建保真度。
英文摘要
Implicit Neural Representations (INRs) have emerged as a compelling paradigm for modeling multidimensional signals by mapping continuous coordinates to signal values. However, Multi-Layer Perceptrons (MLP)-based INRs inherently suffer from spectral bias, which favors low-frequency components and suppresses the reconstruction of essential high-frequency details. While existing techniques, such as Fourier feature mappings, mitigate this issue, they often rely on sensitive manual tuning and are prone to spectral artifacts. In this paper, we propose a spatial-frequency-aware INR framework that combines an MLP branch with a Kolmogorov-Arnold network (KAN) branch for complementary frequency-oriented modeling. The MLP branch provides a low-frequency-oriented representation of smooth structures, whereas the KAN branch complements localized variations and fine details. To coordinate the two branches, we integrate the discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT) into the output fusion stage. The outputs of the two branches are decomposed into wavelet coefficients, and the corresponding coefficients are additively fused before inverse wavelet reconstruction. A wavelet-domain band-separation regularization further penalizes high-frequency responses in the MLP branch and low-frequency responses in the KAN branch, thereby encouraging complementary frequency-oriented behavior. Experiments on 1D signals, 2D images, 3D volumes and signed distance functions, videos, and 4D light-fields demonstrate the applicability of the proposed representation across the evaluated signal modalities. Results demonstrate improved reconstruction fidelity across the evaluated signal modalities.
