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
this https URL

本文提出了一种逐帧融合的、高效灵活的循环网络，以底帧为关键提示，并引入隐式加权损失，用于促进从具有可变数量的输入帧中获取知识，以实现从序列低分辨率和噪声图像重建高分辨率图像的目标。我们在合成和真实数据集上进行了广泛实验，结果表明，我们的方法优于当前最先进的方法。