DemMamba: Alignment-free Raw Video Demoireing with Frequency-assisted Spatio-Temporal Mamba

• URL: http://arxiv.org/abs/2408.10679v1
• Date: Tue, 20 Aug 2024 09:31:03 GMT
• Title: DemMamba: Alignment-free Raw Video Demoireing with Frequency-assisted Spatio-Temporal Mamba
• Authors: Shuning Xu, Xina Liu, Binbin Song, Xiangyu Chen, Qiubo Chen, Jiantao Zhou,
• Abstract summary: We propose an alignment-free Raw video demoireing network with frequency-assisted-temporal Mamba (DemMamba) Our proposed MoiMamba surpasses state-of-the-art approaches by 1.3 dB and delivers a superior visual experience.
• Score: 18.06907326360215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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