Papers › BurstM: Deep Burst Multi-scale SR using Fourier Space with Optical Flow

BurstM: Deep Burst Multi-scale SR using Fourier Space with Optical Flow

21 Sep 2024arXiv:2409.15384archive 2025-07-28

EungGu Kang, Byeonghun Lee, Sunghoon Im, Kyong Hwan Jin

Multi frame super-resolution(MFSR) achieves higher performance than single image super-resolution (SISR), because MFSR leverages abundant information from multiple frames. Recent MFSR approaches adapt the deformable convolution network (DCN) to align the frames. 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. From these problems, existing MFSR approaches struggle to represent high-frequency information. To this end, we propose Deep Burst Multi-scale SR using Fourier Space with Optical Flow (BurstM). The proposed method estimates the optical flow offset for accurate alignment and predicts the continuous Fourier coefficient of each frame for representing high-frequency textures. In addition, we have enhanced the network flexibility by supporting various super-resolution (SR) scale factors with the unimodel. We demonstrate that our method has the highest performance and flexibility than the existing MFSR methods. Our source code is available at https://github.com/Egkang-Luis/burstm

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Code

egkang-luis/burstm officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Burst Image Super-ResolutionImage Super-ResolutionMulti-Frame Super-ResolutionOptical Flow EstimationSuper-Resolution

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Burst Image Super-Resolution BurstSR BurstM PSNR 49.12 #1 of 9 Archive leaderboard report
Burst Image Super-Resolution BurstSR BurstM SSIM 0.987 #1 of 9 Archive leaderboard report
Burst Image Super-Resolution SyntheticBurst BurstM PSNR 42.87 #3 of 8 Archive leaderboard report
Burst Image Super-Resolution SyntheticBurst BurstM SSIM 0.973 #3 of 8 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

ALIGNConvolutionDeformable Convolution

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