Color image-guided very low-resolution depth image reconstruction

Chen, Chen; Lin, Ze; She, Hao; Huang, Yan; Liu, Heng; Wang, Qin; Xie, Sihong

doi:10.1007/s11760-022-02425-7

Cited by 2 publications

(1 citation statement)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhong et al [25] propose a new attention-based multimodal attention fusion strategy, which makes full use of the low-level spatial information and high-level structural information, effectively exploring the complementarity of multi-level multimodal features. Chen et al [26] combine 3D depth images with 2D color images and use the combined image as prior information to guide depth image reconstruction. Guo et al [27] replace the original convolution kernel with a spatial variation kernel so that the HR color image can more effectively guide the LR depth map to restore a high-quality depth map.…”

Section: Color Image-guided Dsr Technologymentioning

confidence: 99%

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

Wang

Huang

2023

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

Section: Color Image-guided Dsr Technologymentioning

confidence: 99%

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

Wang

Huang

2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

Color image guided depth image reconstruction based on a total variation network

Guo,

Xie,

et al. 2023

J. Opt. Soc. Am. A

View full text Add to dashboard Cite

A representative method to improve a depth image is to use an aligned high-quality color image to guide the depth image by migrating the color details to the depth image. In the process of color-guided depth reconstruction, there often is a misalignment of the edge of the color image used to guide the depth image reconstruction and the depth discontinuity of the depth image. This makes the results suffer from texture copy artifacts and blurring depth discontinuities. In this paper, we use a total variation deep network founded on deep learning and high-resolution color images. The experimental result indicates that under the guidance of high-resolution colors, the depth image recovered is closest to the ground truth in the edge contour, the PSNR and FSIM index are suboptimal for 64×, and the contour and position information recovered from the reconstructed depth image can be retained in the very low-resolution depth image.

show abstract

Color image-guided very low-resolution depth image reconstruction

Cited by 2 publications

References 29 publications

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

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

Color image guided depth image reconstruction based on a total variation network

Contact Info

Product

Resources

About