Implicit neural representations (INRs) have garnered significant interest
recently for their ability to model complex, high-dimensional data without
explicit parameterisation. In this work, we introduce TRIDENT, a novel function
for implicit neural representations characterised by a trilogy of
nonlinearities. Firstly, it is designed to represent high-order features
through order compactness. Secondly, TRIDENT efficiently captures frequency
information, a feature called frequency compactness. Thirdly, it has the
capability to represent signals or images such that most of its energy is
concentrated in a limited spatial region, denoting spatial compactness. We
demonstrated through extensive experiments on various inverse problems that our
proposed function outperforms existing implicit neural representation
functions.

通过三个非线性特征，TRIDENT 引入了一种新颖的函数作为隐式神经表示，该函数具有对高阶特征的紧凑表示、对频率信息的高效捕获以及信号或图像在有限空间范围内能量集中的能力，经广泛实验表明，该函数优于现有的隐式神经表示函数。