Cross-domain heterogeneous residual network for single image super-resolution
Ji, Li 1Zhu, Qinghui 1Zhang, Yongqin 1Yin, Juanjuan 1Wei, Ruyi 2Xiao, Jinsheng 2Xiao, Deqiang 3Zhao, Guoying4
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作者信息
1. Sch Informat Sci & Technol,Northwest Univ
2. Elect Informat Sch,Wuhan Univ
3. Sch Opt & Photon,Beijing Inst Technol
4. Ctr Machine Vis & Signal Anal,Univ Oulu
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Abstract
Single image super-resolution is an ill-posed problem, whose purpose is to acquire a high-resolution image from its degraded observation. Existing deep learning-based methods are compromised on their performance and speed due to the heavy design (i.e., huge model size) of networks. In this paper, we propose a novel high-performance cross-domain heterogeneous residual network for super resolved image reconstruction. Our network models heterogeneous residuals between different feature layers by hierarchical residual learning. In outer residual learning, dual-domain enhancement modules extract the frequency-domain information to reinforce the space-domain features of network mapping. In middle residual learning, wide-activated residual-in-residual dense blocks are constructed by concatenating the outputs from previous blocks as the inputs into all subsequent blocks for better parameter efficacy. In inner residual learning, wide-activated residual attention blocks are introduced to capture direction-and location-aware feature maps. The proposed method was evaluated on four benchmark datasets, indicating that it can construct the high-quality super-resolved images and achieve the state-of-the-art performance. Code and pre-trained models are available at https: //github.com/zhangyongqin/HRN. (C)& nbsp;2022 Elsevier Ltd. All rights reserved.
NSTL
Elsevier