In this paper, we propose a reconstruction framework that leverages the Wavelet Scattering Transform (WST) as a multi-scale feature extractor to impose statistical priors under sparse observation conditions. The reconstruction problem is formulated as an optimization task and solved using a neural field, with the WST incorporated into the training loss function. As a proof of concept, we validate the proposed method on HRTF upsampling. A masking strategy is applied to the WST coefficients, resulting in a two-phase procedure. The first phase learns a binary mask from a small multi-subject dataset, while the second phase applies the learned mask to the WST coefficients of an individual HRTF to preserve informative statistical structures during reconstruction. Validation against baseline methods, which also serve as an ablation study of the different components of the framework, demonstrates the effectiveness of the proposed approach.

翻译：本文提出了一种重建框架，利用小波散射变换（WST）作为多尺度特征提取器，在稀疏观测条件下施加统计先验。重建问题被表述为优化任务，并通过神经场求解，同时将WST纳入训练损失函数中。作为概念验证，我们在HRTF上采样中验证了所提方法。对WST系数应用掩蔽策略，形成两阶段流程：第一阶段从小型多主体数据集中学习二元掩膜，第二阶段将学习到的掩膜应用于个体HRTF的WST系数，以在重建过程中保留信息性统计结构。通过与基线方法的对比验证（这些方法同时作为框架各组件的消融研究），证明了所提方法的有效性。