CAttNet: A Compound Attention Network for Depth Estimation of Light Field Images


Dingkang Hua, Qian Zhang, Wan Liao, Bin Wang, and Tao Yan, Journal of Information Processing Systems Vol. 19, No. 4, pp. 483-497, Aug. 2023  

https://doi.org/10.3745/JIPS.02.0201
Keywords: Attention network, Deep Learning, depth estimation, Light Field
Fulltext:

Abstract

Depth estimation is one of the most complicated and difficult problems to deal with in the light field. In this paper, a compound attention convolutional neural network (CAttNet) is proposed to extract depth maps from light field images. To make more effective use of the sub-aperture images (SAIs) of light field and reduce the redundancy in SAIs, we use a compound attention mechanism to weigh the channel and space of the feature map after extracting the primary features, so it can more efficiently select the required view and the important area within the view. We modified various layers of feature extraction to make it more efficient and useful to extract features without adding parameters. By exploring the characteristics of light field, we increased the network depth and optimized the network structure to reduce the adverse impact of this change. CAttNet can efficiently utilize different SAIs correlations and features to generate a high-quality light field depth map. The experimental results show that CAttNet has advantages in both accuracy and time.


Statistics
Show / Hide Statistics

Statistics (Past 3 Years)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.




Cite this article
[APA Style]
Hua, D., Zhang, Q., Liao, W., Wang, B., & Yan, a. (2023). CAttNet: A Compound Attention Network for Depth Estimation of Light Field Images. Journal of Information Processing Systems, 19(4), 483-497. DOI: 10.3745/JIPS.02.0201.

[IEEE Style]
D. Hua, Q. Zhang, W. Liao, B. Wang, a. T. Yan, "CAttNet: A Compound Attention Network for Depth Estimation of Light Field Images," Journal of Information Processing Systems, vol. 19, no. 4, pp. 483-497, 2023. DOI: 10.3745/JIPS.02.0201.

[ACM Style]
Dingkang Hua, Qian Zhang, Wan Liao, Bin Wang, and and Tao Yan. 2023. CAttNet: A Compound Attention Network for Depth Estimation of Light Field Images. Journal of Information Processing Systems, 19, 4, (2023), 483-497. DOI: 10.3745/JIPS.02.0201.