DAEANet: Dual auto-encoder attention network for depth map super-resolution
DAEANet: Dual auto-encoder attention network for depth map super-resolution
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DOI：
10.1016/j.neucom.2021.04.096
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发表时间：
2021-04
期刊：
影响因子：
6
通讯作者：
Xiang Cao;Yihao Luo;Xianyi Zhu;Liangqi Zhang;Yan Xu;Haibo Shen;Tianjiang Wang;Qi Feng
中科院分区：
文献类型：
--
作者：
Xiang Cao;Yihao Luo;Xianyi Zhu;Liangqi Zhang;Yan Xu;Haibo Shen;Tianjiang Wang;Qi Feng
Recently, depth map super-resolution (DSR) has obtained remarkable performance with the development of convolutional neural networks (CNNs). High-resolution (HR) depth map can be inferred from a low-resolution (LR) one with the guidance of its corresponding HR intensity image. However, most of the existing CNNs-based methods unilaterally transfer structures information of guidance image to the input depth map, which ignores the corresponding relations between the depth map and the intensity map. In this paper, we propose a novel dual auto-encoder attention network (DAEANet) for DSR. The proposed DAEANet includes two auto-encoder networks, where guidance auto-encoder network (GAENet) and target auto-encoder network (TAENet) aim to extract feature information from intensity image and depth map. Specifically, all auto-encoder networks are similar and trained simultaneously to ensure structural consistency. Furthermore, to preserve the structure information in the process of training, the attention mechanism is employed to our DAEANet. Extensive experiments on several popular benchmarks show that the proposed DAEANet outperforms existing state-of-the-art algorithms.
