RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution
This addresses the problem of enhancing image quality for smartphone cameras with limited sensors, representing an incremental improvement over existing burst super-resolution techniques.
The paper tackles burst super-resolution by proposing an efficient recurrent network that uses a base-frame as a prompt to guide fusion of complementary information from multiple low-resolution images, achieving better results than state-of-the-art methods on synthetic and real-world datasets.
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.