FMA-Net++: Motion- and Exposure-Aware Real-World Joint Video Super-Resolution and Deblurring

• URL: http://arxiv.org/abs/2512.04390v1
• Date: Thu, 04 Dec 2025 02:23:52 GMT
• Title: FMA-Net++: Motion- and Exposure-Aware Real-World Joint Video Super-Resolution and Deblurring
• Authors: Geunhyuk Youk, Jihyong Oh, Munchurl Kim,
• Abstract summary: We present FMA-Net++, a framework for joint video super-resolution and deblurring.<n>It explicitly models this coupled effect of motion and dynamically varying exposure.<n>It achieves state-of-the-art accuracy and temporal consistency on our new benchmarks and GoPro.
• Score: 33.16054147490686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world video restoration is plagued by complex degradations from motion coupled with dynamically varying exposure - a key challenge largely overlooked by prior works and a common artifact of auto-exposure or low-light capture. We present FMA-Net++, a framework for joint video super-resolution and deblurring that explicitly models this coupled effect of motion and dynamically varying exposure. FMA-Net++ adopts a sequence-level architecture built from Hierarchical Refinement with Bidirectional Propagation blocks, enabling parallel, long-range temporal modeling. Within each block, an Exposure Time-aware Modulation layer conditions features on per-frame exposure, which in turn drives an exposure-aware Flow-Guided Dynamic Filtering module to infer motion- and exposure-aware degradation kernels. FMA-Net++ decouples degradation learning from restoration: the former predicts exposure- and motion-aware priors to guide the latter, improving both accuracy and efficiency. To evaluate under realistic capture conditions, we introduce REDS-ME (multi-exposure) and REDS-RE (random-exposure) benchmarks. Trained solely on synthetic data, FMA-Net++ achieves state-of-the-art accuracy and temporal consistency on our new benchmarks and GoPro, outperforming recent methods in both restoration quality and inference speed, and generalizes well to challenging real-world videos.

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