Towards Real-World Burst Image Super-Resolution: Benchmark and Method
Pengxu Wei Yujing Sun Xingbei Guo Chang Liu Jie Chen Xiangyang Ji Liang Lin
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. In this paper, we establish
a large-scale real-world burst super-resolution dataset, i.e., RealBSR, to
explore the faithful reconstruction of image details from multiple frames.
Furthermore, we introduce a Federated Burst Affinity network (FBAnet) to
investigate non-trivial pixel-wise displacements among images under real-world
image degradation. Specifically, rather than using pixel-wise alignment, our
FBAnet employs a simple homography alignment from a structural geometry aspect
and a Federated Affinity Fusion (FAF) strategy to aggregate the complementary
information among frames. Those fused informative representations are fed to a
Transformer-based module of burst representation decoding. Besides, we have
conducted extensive experiments on two versions of our datasets, i.e.,
RealBSR-RAW and RealBSR-RGB. Experimental results demonstrate that our FBAnet
outperforms existing state-of-the-art burst SR methods and also achieves
visually-pleasant SR image predictions with model details. Our dataset, codes,
and models are publicly available at https://github.com/yjsunnn/FBANet.