Burst Image Super-Resolution

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

2 leaderboard tables shown for this task, 2 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

10 shown of 10 papers with code (15 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Deep Burst Super-Resolution
  26 Jan 2021 3 repositories listed Syntology ran 6 of 7 samples · 1 unverified · 7 pointer-only (licence)
  We propose a novel architecture for the burst super-resolution task.
• Deep Reparametrization of Multi-Frame Super-Resolution and Denoising
  18 Aug 2021 2 repositories listed
  The deep reparametrization allows us to directly model the image formation process in the latent space, and to integrate learned image priors into the prediction.
• EBSR: Feature Enhanced Burst Super-Resolution With Deformable Alignment
  15 Jun 2021 2 repositories listed
  We propose a novel architecture to handle the problem of multi-frame super-resolution (MFSR).
• BurstM: Deep Burst Multi-scale SR using Fourier Space with Optical Flow
  21 Sep 2024 1 repository listed
  However, the existing MFSR suffers from misalignments between the reference and source frames due to the limitations of DCN, such as small receptive fields and the predefined number of kernels.
• Simple Base Frame Guided Residual Network for RAW Burst Image Super-Resolution
  27 Feb 2024 1 repository listed
  Burst super-resolution or multi-frame super-resolution (MFSR) has gained significant attention in recent years, particularly in the context of mobile photography.
• Towards Real-World Burst Image Super-Resolution: Benchmark and Method
  9 Sep 2023 1 repository listed Syntology ran 22 of 29 samples · 7 unverified
  Despite substantial advances, single-image super-resolution (SISR) is always in a dilemma to reconstruct high-quality images with limited information from one input image, especially in realistic scenarios.
• BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable Alignment
  18 Apr 2022 1 repository listed Syntology ran 5 of 13 samples · 8 unverified
  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.
• Kernel-aware Burst Blind Super-Resolution
  14 Dec 2021 1 repository listed
  In this paper, we address the problem of reconstructing HR images from raw burst sequences acquired from a modern handheld device.
• Burst Image Restoration and Enhancement
  7 Oct 2021 1 repository listed
  Our central idea is to create a set of pseudo-burst features that combine complementary information from all the input burst frames to seamlessly exchange information.
• Unfolding the Alternating Optimization for Blind Super Resolution
  6 Oct 2020 1 repository listed Syntology ran 5 of 5 samples · 0 unverified · 5 pointer-only (licence)
  More importantly, \textit{Restorer} is trained with the kernel estimated by \textit{Estimator}, instead of ground-truth kernel, thus \textit{Restorer} could be more tolerant to the estimation error of \textit{Estimator}.

Syntology lines on 4 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.