Depth Map Super-Resolution Reconstruction Based on Multi-Channel Progressive Attention Fusion Network

Depth maps captured by traditional consumer-grade depth cameras are often noisy and low-resolution. Especially when upsampling low-resolution depth maps with large upsampling factors, the resulting depth maps tend to suffer from vague edges. To address these issues, we propose a multi-channel progressive attention fusion network that utilizes a pyramid structure to progressively recover high-resolution depth maps. The inputs of the network are the low-resolution depth image and its corresponding color image. The color image is used as prior information in this network to fill in the missing high-frequency information of the depth image. Then, an attention-based multi-branch feature fusion module is employed to mitigate the texture replication issue caused by incorrect guidance from the color image and inconsistencies between the color image and the depth map. This module restores the HR depth map by effectively integrating the information from both inputs. Extensive experimental results demonstrate that our proposed method outperforms existing methods.