mlsanityData to 7 Oct 2026

RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution

arXiv 2306.17595 ↗First posted 30 Jun 2023cs.CVcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 45 of its 58 references that are in the map. First posted on arXiv 30 Jun 2023.

Builds on

45 of 58 references.

References · 45

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33 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 0; 2026: 2.

5 papers.

  1. 2024QMambaBSR: Burst Image Super-Resolution with Query State Space Modelcited by 27
  2. 2024Self-Supervised Video Desmoking for Laparoscopic Surgerycited by 10
  3. 2024NIR-Assisted Image Denoising: A Selective Fusion Approach and A Real-World Benchmark Datasetcited by 4
  4. 2026Dynamic Exposure Burst Image Restorationcited by 0
  5. 2026Bricker to BRACE: A Bracket Exposure RAW Dataset and Restoration Model for Flicker-Bandingcited by 0