We propose the Subtractive Modulative Network (SMN), a novel, parameter-efficient Implicit Neural Representation (INR) architecture inspired by classical subtractive synthesis. The SMN is designed as a principled signal processing pipeline, featuring a learnable periodic activation layer (Oscillator) that generates a multi-frequency basis, and a series of modulative mask modules (Filters) that actively generate high-order harmonics. We provide both theoretical analysis and empirical validation for our design. Our SMN achieves a PSNR of $40+$ dB on two image datasets, comparing favorably against state-of-the-art methods in terms of both reconstruction accuracy and parameter efficiency. Furthermore, consistent advantage is observed on the challenging 3D NeRF novel view synthesis task. Supplementary materials are available at https://inrainbws.github.io/smn/.

翻译：我们提出了一种新颖的参数高效隐式神经表示（INR）架构——减性调制网络（SMN），其灵感来源于经典减性合成方法。SMN被设计为一个结构化的信号处理流程，包含一个可学习的周期性激活层（振荡器）用于生成多频基，以及一系列调制掩码模块（滤波器）用于主动生成高阶谐波。我们为这一设计提供了理论分析和实验验证。在两个图像数据集上，我们的SMN实现了超过40 dB的峰值信噪比（PSNR），在重建精度和参数效率方面均优于现有先进方法。此外，在具有挑战性的3D神经辐射场（NeRF）新视角合成任务上也观察到了持续的优势。补充材料详见https://inrainbws.github.io/smn/。