MRANet: Multi-atrous residual attention Network for stereo image super-resolution |
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Affiliation: | 1. Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China;2. Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing, China;3. Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing Jiaotong University, Beijing 100044, China;4. Xiamen Engineering Research Center of Intelligent Traffic Guidance Technology, School of Computer and Information Engineering, Xiamen University of Technology, Fujian 361024, China;1. College of Information Science and Engineering, Ocean University of China, Qingdao 266000, China;2. Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266000, China |
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Abstract: | In recent years, stereo cameras have been widely used in various fields. Due to the limited resolution of real equipments, stereo image super-resolution (SR) is a very important and hot topic. Recent studies have shown that deep network structures can directly affect feature expression and extraction and thus influence the final results. In this paper, we propose a multi-atrous residual attention stereo super-resolution network (MRANet) with parallax extraction and strong discriminative ability. Specifically, we propose a multi-scale atrous residual attention (MARA) block to obtain receptive fields of different scales through a multi-scale atrous convolution and then combine them with attention mechanisms to extract more diverse and meaningful information. Moreover, we propose a stereo feature fusion unit for stereo parallax extraction and single viewpoint feature refinement and integration. Experiments on benchmark datasets show that MRANet achieves state-of-the-art performance in terms of quantitative metrics and visual quality compared with several SR methods. |
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Keywords: | Stereo cameras Stereo image super-resolution Discriminative ability Parallax extraction Attention mechanism |
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