WAFP-Net: Weighted Attention Fusion Based Progressive Residual Learning for Depth Map Super-Resolution

Despite the remarkable progresses achieved in depth map super-resolution (DSR), it remains a major challenge to tackle with real-world degradation of low-resolution (LR) depth maps. Synthetic datasets are mainly used in existing DSR approaches, which is quite different from what would get from a real depth sensor. Besides, the enhancements of features in existing DSR approaches are not sufficiently enough, which also limit the performance. To alleviate these problems, we first propose two types of degradation models to describe the generation of LR depth maps, including bi-cubic down-sampling with noise and interval down-sampling, and different DSR models are learned correspondingly. Then, we propose a weighted attention fusion strategy that is embedded into a progressive residual learning framework, which guarantees that the high-resolution (HR) depth maps can be well recovered in a coarse-to-fine manner. The weighted attention fusion strategy can enhance the features with abundant high-frequency components in both global and local manners, thus better HR depth maps can be expected. Besides, to re-use the effective information in the progressive process sufficiently, a multi-stage fusion module is combined into the proposed framework, and the Total Generalized Variation (TGV) regularization and input loss are exploited to further improve the performance of our method. Extensive experiments of different benchmarks demonstrate the superiority of our approach over the state-of-the-art (SOTA) approaches.