DemMamba: Alignment-free Raw Video Demoireing with Frequency-assisted Spatio-Temporal Mamba
<|reference_start|>DemMamba: Alignment-free Raw Video Demoireing with Frequency-assisted Spatio-Temporal Mamba: Moire patterns arise when two similar repetitive patterns interfere, a phenomenon frequently observed during the capture of images or videos on screens. The color, shape, and location of moire patterns may differ across video frames, posing a challenge in learning information from adjacent frames and preserving temporal consistency. Previous video demoireing methods heavily rely on well-designed alignment modules, resulting in substantial computational burdens. Recently, Mamba, an improved version of the State Space Model (SSM), has demonstrated significant potential for modeling long-range dependencies with linear complexity, enabling efficient temporal modeling in video demoireing without requiring a specific alignment module. In this paper, we propose a novel alignment-free Raw video demoireing network with frequency-assisted spatio-temporal Mamba (DemMamba). The Spatial Mamba Block (SMB) and Temporal Mamba Block (TMB) are sequentially arranged to facilitate effective intra- and inter-relationship modeling in Raw videos with moire patterns. Within SMB, an Adaptive Frequency Block (AFB) is introduced to aid demoireing in the frequency domain. For TMB, a Channel Attention Block (CAB) is embedded to further enhance temporal information interactions by exploiting the inter-channel relationships among features. Extensive experiments demonstrate that our proposed DemMamba surpasses state-of-the-art approaches by 1.3 dB and delivers a superior visual experience.<|reference_end|>
@article{xu2024demmamba:,
    title={DemMamba: Alignment-free Raw Video Demoireing with Frequency-assisted
  Spatio-Temporal Mamba},
    author={Shuning Xu, Xina Liu, Binbin Song, Xiangyu Chen, Qiubo Chen, Jiantao
  Zhou},
    journal={arXiv preprint arXiv:2408.10679},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10679},
    primaryClass={cs.CV}
}