Deep Hybrid Architecture for Very Low-Resolution Image Classification Using Capsule Attention

Despite extensive applications in surveillance and remote sensing, research on very low-resolution (VLR) image classification remains relatively unexplored in comparison to high-resolution (HR) image classification. We introduce a deep hybrid network that integrates capsule routing networks with a two-layer attention module. In the proposed architecture, the attention mechanism captures the more salient features, and the capsule network encodes these features to be robust to resolution changes. To enhance the network's performance, a transfer learning on a custom image dataset, which is well-aligned to CIFAR-10, is utilized. The proposed model (Codes for the models are available at: https://github.com/kdhasi/Deep-CapsuleAttention.git) is evaluated on two VLR classification tasks of 'VLR complex image' and 'VLR real-world digit'. Experimental results demonstrate the superiority of the proposed model, achieving state-of-the-art (SOTA) results in both VLR complex image and VLR real-world digit domains while using fewer parameters compared to previous SOTA networks. Specifically, on the VLR CIFAR-10 dataset, the proposed model attains a 3.17% improvement in detection accuracy over the current benchmarks, and, on the VLR SVHN dataset, it achieves a 3.85% improvement by using 80% fewer parameters.