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Multi-prior guided depth map super-resolution based on a diffusion model
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AI
DOI：10.1007/s00371-025-04179-y.png)
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Abstract
En 中文
Guided depth super-resolution (GDSR) aims to reconstruct high-resolution (HR) depth maps from low-resolution (LR) counterparts with the aid of aligned HR RGB images. However, existing methods exhibit limited capability in learning and representing prior knowledge and high-frequency components, often resulting in degraded structural accuracy and detail fidelity. Moreover, most current approaches lack effective integration of prior information and struggle to recover fine-grained details. To address these limitations, we propose a novel multi-prior guided depth super-resolution framework based on diffusion model. Specifically, a multi-prior guided information extraction block is designed to extract color and edge priors, providing complementary high-frequency guidance. We further introduce a multi-headed channel-wise self-attention (MCSA) module and a feature optimized selection module (FOSM) to enhance feature extraction and preserve critical information. Besides, reconstruction module based on diffusion model is employed to denoise and generate high-quality depth maps, ensuring spatial consistency and edge sharpness. Extensive experiments demonstrate that our proposed method outperforms existing state-of-the-art techniques in both accuracy and visual quality.
Keywords:
Guided depth super-resolution
Self-attention
Diffusion model
Multi-prior guided
Journal
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