Frequency-Aware Implicit Neural Representation for Multi-Dimensional Data Recovery

Implicit neural representation (INR) has emerged as a powerful representation for data (e.g., multispectral images and videos). Previously, most INR methods directly represent data in the original space. However, since different frequency components are mixed in the original space, it is difficult to capture these frequency components simultaneously and accurately. To alleviate this limitation, we suggest a new frequency-aware implicit neural representation (FA-INR) working in a physically interpretable and learnable frequency space by cleverly introducing an extra frequency dimension, which allows us to readily decouple and modulate different frequency components, leading to a more accurate characterization of different frequency components in a divide-and-conquer manner. Specifically, the FA-INR consists of two important modules, i.e., the frequency module and the integration module. In the frequency module, we propose a new low-rank tensor frequency function to compactly and continuously represent the latent frequency space. In the integration module, different frequency components are adaptively integrated back to the original space. Extensive experiments on various multi-dimensional data, including multispectral images, color videos, and light field data, demonstrate that the proposed FA-INR significantly outperforms the state-of-the-art INR methods, especially for characterizing high-frequency components (e.g., textures and edges).