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Single-pixel imaging gets a deep learning boost: SISTA-Net leverages learned sparsity and hybrid CNN-VSSM architectures to achieve state-of-the-art reconstruction quality, even in noisy underwater environments.
UAV swarms can achieve near-optimal cooperative deployment and generalize to new team sizes using a communication-aware MARL approach, even with limited communication and partial observability.
Achieve real-time (409 FPS) underwater image enhancement with a tiny (3,880 parameter) model that significantly improves color accuracy, enabling deployment on resource-constrained underwater platforms.
