论文标题
BSRT：使用SWIN Transformer和流量引导的可变形对齐方式改善爆发超分辨率
BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable Alignment
论文作者
论文摘要
这项工作使用新体系结构解决了爆发超级分辨率（爆炸）任务，该任务需要从一系列嘈杂，未对准和低分辨率的原始爆发序列中恢复高质量的图像。为了克服爆发中的挑战，我们提出了一个爆发超分辨率变压器（BSRT），该变压器可以显着提高提取框架间信息和重建的能力。为了实现这一目标，我们提出了金字塔引导的可变形卷积网络（金字塔FG-DCN），并将Swin变压器块和组纳入我们的主要骨架。更具体地说，我们将光流和可变形的卷积结合在一起，因此我们的BSRT可以更有效地处理未对准并汇总潜在的纹理信息。此外，我们的基于变压器的结构可以捕获远程依赖性，以进一步提高性能。对合成和现实世界轨道的评估表明，我们的方法在爆发任务中实现了新的最新技术。此外，我们的BSRT在NTIRE2022爆发超级分辨率挑战中赢得了冠军。
This work addresses the Burst Super-Resolution (BurstSR) task using a new architecture, which requires restoring a high-quality image from a sequence of noisy, misaligned, and low-resolution RAW bursts. To overcome the challenges in BurstSR, we propose a Burst Super-Resolution Transformer (BSRT), which can significantly improve the capability of extracting inter-frame information and reconstruction. To achieve this goal, we propose a Pyramid Flow-Guided Deformable Convolution Network (Pyramid FG-DCN) and incorporate Swin Transformer Blocks and Groups as our main backbone. More specifically, we combine optical flows and deformable convolutions, hence our BSRT can handle misalignment and aggregate the potential texture information in multi-frames more efficiently. In addition, our Transformer-based structure can capture long-range dependency to further improve the performance. The evaluation on both synthetic and real-world tracks demonstrates that our approach achieves a new state-of-the-art in BurstSR task. Further, our BSRT wins the championship in the NTIRE2022 Burst Super-Resolution Challenge.
