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

Moire patterns, resulting from the interference of two similar repetitive patterns, are frequently observed during the capture of images or videos on screens. These patterns vary in color, shape, and location across video frames, posing challenges in extracting information from adjacent frames and preserving temporal consistency throughout the restoration process. Existing deep learning methods often depend on well-designed alignment modules, such as optical flow estimation, deformable convolution, and cross-frame self-attention layers, incurring high computational costs. Recent studies indicate that utilizing raw data as input can significantly improve the effectiveness of video demoireing by providing the pristine degradation information and more detailed content. However, previous works fail to design both efficient and effective raw video demoireing methods that can maintain temporal consistency and prevent degradation of color and spatial details. This paper introduces a novel alignment-free raw video demoireing network with frequency-assisted spatio-temporal Mamba (DemMamba). It features sequentially arranged Spatial Mamba Blocks (SMB) and Temporal Mamba Blocks (TMB) to effectively model the inter- and intra-relationships in raw videos affected by moire patterns. An Adaptive Frequency Block (AFB) within the SMB facilitates demoireing in the frequency domain, while a Channel Attention Block (CAB) in the TMB enhances the temporal information interactions by leveraging inter-channel relationships among features. Extensive experiments demonstrate that our proposed DemMamba surpasses state-of-the-art methods by 1.3 dB in PSNR, and also provides a satisfactory visual experience.