Burst Image Super-Resolution with Mamba

• URL: http://arxiv.org/abs/2503.19634v1
• Date: Tue, 25 Mar 2025 13:22:55 GMT
• Title: Burst Image Super-Resolution with Mamba
• Authors: Ozan Unal, Steven Marty, Dengxin Dai,
• Abstract summary: Burst super-resolution (BISR) aims to enhance the resolution of a image by leveraging information from multiple low-resolution images captured in quick succession.<n>In this work, we introduce BurstambaM, a Mamba-based architecture for BISR.<n>Our approach decouples the task into two specialized branches: a spatial module for super-resolution and a temporal module for subpixel prior extraction.
• Score: 43.80081193548115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Burst image super-resolution (BISR) aims to enhance the resolution of a keyframe by leveraging information from multiple low-resolution images captured in quick succession. In the deep learning era, BISR methods have evolved from fully convolutional networks to transformer-based architectures, which, despite their effectiveness, suffer from the quadratic complexity of self-attention. We see Mamba as the next natural step in the evolution of this field, offering a comparable global receptive field and selective information routing with only linear time complexity. In this work, we introduce BurstMamba, a Mamba-based architecture for BISR. Our approach decouples the task into two specialized branches: a spatial module for keyframe super-resolution and a temporal module for subpixel prior extraction, striking a balance between computational efficiency and burst information integration. To further enhance burst processing with Mamba, we propose two novel strategies: (i) optical flow-based serialization, which aligns burst sequences only during state updates to preserve subpixel details, and (ii) a wavelet-based reparameterization of the state-space update rules, prioritizing high-frequency features for improved burst-to-keyframe information passing. Our framework achieves SOTA performance on public benchmarks of SyntheticSR, RealBSR-RGB, and RealBSR-RAW.

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