用分层网络提升小模型对科学数据全频谱细节的还原能力
Multi-resolution Enhancement for Full Spectrum Neural Representations
- 分尺度构建层级结构，让小模型也能捕捉多尺度信息
- 在多个实验数据集上实现高保真压缩，存储成本降低但精度不降
- 适合需要高效存储与高精度重建的科学数据处理场景
科学数据采集速度持续超过存储与分析能力，体素表示日益难以处理。隐式神经表示（INRs）通过基于坐标的神经网络编码信号，作为数据替代品，其计算与存储开销随网络复杂度增长，而非数据维度。然而，小型INRs难以忠实再现多尺度结构、高频信息和精细纹理，这些在科学测量中占很大比例。我们提出WIEN-INR，一种理论指导的分层INR框架，将建模分布于不同分辨率尺度，并引入新型增强网络以恢复细微细节。该多尺度架构使小型网络在保持训练效率的同时，仍能保留全频谱信息，降低存储成本。在跨尺度、多复杂度的原始实验数据上评估，WIEN-INR为神经表示在科学工作流中的广泛应用提供了可行路径，实现了紧凑、鲁棒且高保真的表示。
原文摘要 · Abstract (English)
Scientific data acquisition continues to outpace storage and analysis capabilities, making voxel-based representations increasingly intractable. Implicit neural representations (INRs) offer a promising solution by encoding signals through coordinate-based neural networks, serving as surrogates of data, with computational and storage requirements scaling with network complexity rather than data dimensionality. However, smaller INRs struggle to faithfully represent the multi-scale structures, high-frequency information, and fine textures that constitute a large proportion of scientific measurements. We propose WIEN-INR, a theoretically-guided hierarchical INR framework that distributes modeling across resolution scales and enables improved representation capacity through a novel enhancement network to recover subtle details. This multi-scale architecture allows smaller networks to retain the full spectrum of information while preserving training efficiency and lowering storage cost. Evaluated on distinct raw experimental measurements across scales and complexities, WIEN-INR represents a practical step toward broader adoption of neural representations in scientific workflows, delivering compact, robust, and high-fidelity representations.
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