用光流与傅里叶系数提升多帧超分精度，支持任意缩放倍数。
BurstM: Deep Burst Multi-scale SR using Fourier Space with Optical Flow
- 结合光流精对齐帧，用连续傅里叶系数捕捉高频纹理
- 在Real-World Burst、Urban100等数据集上优于现有方法，PSNR提升0.1~0.3dB
- 单模型支持多种缩放因子，部署灵活，适合真实场景图像增强
多帧超分辨率（MFSR）通过利用多帧间的丰富信息，性能优于单图超分辨率（SISR）。现有MFSR方法采用可变形卷积网络（DCN）进行帧对齐，但受限于小感受野和预设卷积核数量，易产生参考帧与源帧的错位，难以有效表示高频信息。为此，我们提出基于傅里叶空间与光流的深度多帧超分辨率方法（BurstM）。该方法通过估计光流偏移实现精准对齐，并预测每帧的连续傅里叶系数以表征高频纹理。此外，通过统一模型结构，实现了对多种超分辨率缩放因子的支持，提升了网络灵活性。实验表明，BurstM在Real-World Burst、Urban100、BSD100等数据集上均取得最优性能，相比现有方法平均提升0.1~0.3dB PSNR。代码已开源：https://github.com/Egkang-Luis/burstm。
原文摘要 · Abstract (English)
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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