Dual Graph Regularized Deep Unfolding Network for Guided Depth Map Super-resolution

Depth map super-resolution with color guidance is a fundamental task in computer vision that aims to reconstruct high-resolution depth maps by leveraging structural correlations from corresponding guidance images. Recently, with the development of deep learning techniques, the performance of guided depth super-resolution (GDSR) models has been significantly improved. However, most existing approaches rely on black-box architectures that lack theoretical interpretability. Although graph optimization has been explored to integrate model-driven and data-driven frameworks, it remains computationally expensive and struggles to preserve the intrinsic structures of the depth maps. To overcome these limitations, we propose a novel GDSR framework based on a dual graph Laplacian prior, termed LapNet, which efficiently unfolds graph optimization into a deep neural network. Specifically, we first formulate a dual graph Laplacian prior that separately models structural dependencies along the row and column dimensions of the depth maps. This formulation explicitly enforces piecewise smoothness while reducing computational complexity from O(H 3 W 3 ) to O(H 3 + W 3 ) by avoiding the construction of global affinity graph. Furthermore, we develop a deep implicit prior to extract high-frequency structural cues from the guidance image, serving as a complementary component to the manually designed prior. Finally, we integrate these complementary priors into a unified variational optimization framework, which is efficiently solved through alternating minimization and subsequently unfolded into an interpretable multi-stage deep network. Extensive experiments on both synthetic and real-world datasets demonstrate that LapNet achieves state-of-the-art performance while maintaining low computational complexity.