Guided Depth Map Super-Resolution via Multi-Scale Fusion U-shaped Mamba Network
Chenggang Guo, Hao Xu, XianMing Wan

TL;DR
This paper introduces the MSF-UM model, a multi-scale fusion U-shaped network that combines local convolutional features with efficient state-space modeling to enhance depth map super-resolution guided by color images, achieving better accuracy with fewer parameters.
Contribution
The paper proposes a novel MSF-UM framework integrating Mamba's state-space modeling into a multi-scale U-shaped structure for depth super-resolution, reducing parameters and improving accuracy.
Findings
Outperforms existing methods in accuracy
Reduces model parameters significantly
Shows strong generalization on large-scale datasets
Abstract
Depth map super-resolution technology aims to improve the spatial resolution of low-resolution depth maps and effectively restore high-frequency detail information. Traditional convolutional neural network has limitations in dealing with long-range dependencies and are unable to fully model the global contextual information in depth maps. Although transformer can model global dependencies, its computational complexity and memory consumption are quadratic, which significantly limits its ability to process high-resolution depth maps. In this paper, we propose a multi-scale fusion U-shaped Mamba (MSF-UM) model, a novel guided depth map super-resolution framework. The core innovation of this model is to integrate Mamba's efficient state-space modeling capabilities into a multi-scale U-shaped fusion structure guided by a color image. The structure combining the residual dense channel…
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Taxonomy
TopicsAdvanced Vision and Imaging · Advanced Image Processing Techniques · Image Processing Techniques and Applications
