Burst super-resolution (BurstSR) aims at reconstructing a high-resolution (HR) image from a sequence of low-resolution (LR) and noisy images, which is conducive to enhancing the imaging effects of smartphones with limited sensors. The main challenge of BurstSR is to effectively combine the complementary information from input frames, while existing methods still struggle with it. In this paper, we suggest fusing cues frame-by-frame with an efficient and flexible recurrent network. In particular, we emphasize the role of the base-frame and utilize it as a key prompt to guide the knowledge acquisition from other frames in every recurrence. Moreover, we introduce an implicit weighting loss to improve the model's flexibility in facing input frames with variable numbers. Extensive experiments on both synthetic and real-world datasets demonstrate that our method achieves better results than state-of-the-art ones. Codes and pre-trained models are available at https://github.com/ZcsrenlongZ/RBSR.

翻译：突发超分辨率（BurstSR）旨在从一序列低分辨率（LR）且含噪声的图像中重建高分辨率（HR）图像，这有助于提升配备有限传感器的智能手机的成像效果。BurstSR的主要挑战在于有效融合输入帧中的互补信息，而现有方法仍难以解决此问题。本文提出一种高效灵活的循环网络，通过逐帧融合线索处理该任务。具体地，我们强调基础帧的作用，并将其作为关键提示，在每次循环中引导从其他帧获取知识。此外，我们引入隐式加权损失，以增强模型面对可变数量输入帧时的灵活性。在合成与真实世界数据集上的广泛实验表明，我们的方法取得了优于现有最优方法的结果。代码与预训练模型已开源至 https://github.com/ZcsrenlongZ/RBSR。