Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution

• URL: http://arxiv.org/abs/2006.01469v1
• Date: Tue, 2 Jun 2020 09:12:23 GMT
• Title: Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution
• Authors: Xibin Song, Yuchao Dai, Dingfu Zhou, Liu Liu, Wei Li, Hongdng Li, Ruigang Yang
• Abstract summary: We argue that DSR models trained on synthetic dataset are restrictive and not effective in dealing with real-world DSR tasks. We make two contributions in tackling real-world degradation of different depth sensors. We propose a new framework for real-world DSR, which consists of four modules.
• Score: 58.626803922196146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the remarkable progresses made in deep-learning based depth map super-resolution (DSR), how to tackle real-world degradation in low-resolution (LR) depth maps remains a major challenge. Existing DSR model is generally trained and tested on synthetic dataset, which is very different from what would get from a real depth sensor. In this paper, we argue that DSR models trained under this setting are restrictive and not effective in dealing with real-world DSR tasks. We make two contributions in tackling real-world degradation of different depth sensors. First, we propose to classify the generation of LR depth maps into two types: non-linear downsampling with noise and interval downsampling, for which DSR models are learned correspondingly. Second, we propose a new framework for real-world DSR, which consists of four modules : 1) An iterative residual learning module with deep supervision to learn effective high-frequency components of depth maps in a coarse-to-fine manner; 2) A channel attention strategy to enhance channels with abundant high-frequency components; 3) A multi-stage fusion module to effectively re-exploit the results in the coarse-to-fine process; and 4) A depth refinement module to improve the depth map by TGV regularization and input loss. Extensive experiments on benchmarking datasets demonstrate the superiority of our method over current state-of-the-art DSR methods.

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