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.
Abstract (translated)
现实世界的视频修复面临由于运动与动态变化曝光相互作用而导致的复杂退化问题——这一挑战被之前的大部分工作忽视，且通常是自动曝光或低光捕捉下的常见缺陷。我们提出了FMA-Net++框架，该框架用于联合进行视频超分辨率和去模糊处理，并明确地建模了运动与动态变化曝光相结合的效果。 FMA-Net++采用了一种基于双向传播块的层级细化序列级架构，这使得长期时序并行模型得以实现。在每个模块中，一种带有曝光时间感知调节层（Exposure Time-aware Modulation layer）的结构根据每一帧的曝光条件来调整特征。这一机制驱动了一个带曝光意识的流导向动态滤波模块（exposure-aware Flow-Guided Dynamic Filtering module），用于推断运动和曝光相关的退化内核。 FMA-Net++将退化的学习与修复过程解耦：前者预测带有曝光和运动感知的先验信息，以指导后者进行更准确且高效的恢复。为了评估在现实捕捉条件下的性能，我们引入了REDs-ME（多曝光）和REDs-RE（随机曝光）基准测试。 仅使用合成数据训练后，FMA-Net++在我们的新基准以及GoPro上实现了最新的精度与时序一致性表现，在修复质量和推理速度方面优于近期方法，并且能很好地推广到具有挑战性的现实视频中。
URL
https://arxiv.org/abs/2512.04390
