FR-CapsNet: Enhancing Low-Resolution Image Classification via Frequency Routed Capsules

Very low-resolution (VLR) images present a significant challenge for deep classification networks due to their inherent lack of fine spatial detail. While capsule networks (CapsNets), which encode spatial and pose information, are robust to resolution changes, they often struggle to perform and scale effectively on complex datasets. In this study, we introduce a novel frequency routing-based CapsNet (FR-CapsNet) to replace the conventional spatial routing in CapsNets. Though VLR images lose fine grained features, they retain high-level features captured by the low-frequency components. By computing capsule activation and pose information in the frequency domain and subsequently encoding them in the spatial domain, FR-CapsNet improves robustness to resolution degradation. Furthermore, our method utilizes a global routing framework that considerably reduces computational demands, enabling FR-CapsNet to scale effectively to larger and more diverse datasets. FR-CapsNet outperforms state-of-the-art (SOTA) convolutional neural networks (CNNs), other CapsNets, Transformers, and other advanced architectures in real-world VLR digit and image classification tasks. Specifically, on the VLR CIFAR-10 dataset, FR-CapsNet surpasses the current benchmark by 4.77% while using 4 times fewer parameters. Similarly, on the VLR SVHN and CIFAR-100 datasets, it exceeds the benchmark by 0.27% and 1.55%, respectively. Extensive experiments further demonstrate the superior generalization and robustness of FR-CapsNet compared to other SOTA methods. The codes for our models are available at XX.