Deep multi‐level up‐projection network for single image super‐resolution

Recently, a method called Meta-SR has solved the problem of super-resolution of arbitrary scale factor with only one single model. However, it has a limited reconstruction accuracy compared with RDN[Formula: see text] and EDSR[Formula: see text]. Inspired by Meta-SR, we noticed that by combining the core idea of Meta-SR and D-DBPN, we might construct a network that has as good image reconstruction accuracy as D-DBPN’s, at the same time, keeps arbitrary scaling function. According to Meta-SR’s Meta-Upscale Module, we designed a different structure called Meta-Downscale Module. By using these two different modules and back-projection structure, we construct an arbitrary back-projection network, which has the ability to enlarge images with arbitrary scale factor by using only one single model, meanwhile, obtains state-of-the-art reconstruction results. Through extensive experiments, our proposed method performs better reconstruction effect than Meta-SR and more efficient than D-DBPN. Besides that, we also evaluated the proposed method on widely used benchmark dataset on single image super-resolution. The experimental results show the superiority of our model compared to RDN+ and EDSR+.

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Single image super-resolution with multi-level feature fusion recursive network

Neurocomputing ◽

10.1016/j.neucom.2019.06.102 ◽

2019 ◽

Vol 370 ◽

pp. 166-173 ◽

Cited By ~ 4

Author(s):

Xin Jin ◽

Qiming Xiong ◽

Chengyi Xiong ◽

Zhibang Li ◽

Zhirong Gao

Keyword(s):

Feature Fusion ◽

Super Resolution ◽

Single Image ◽

Image Super Resolution ◽

Multi Level ◽

Single Image Super Resolution

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Efficient Single Image Super-Resolution via Hybrid Residual Feature Learning with Compact Back-Projection Network

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) ◽

10.1109/iccvw.2019.00300 ◽

2019 ◽

Author(s):

Feiyang Zhu ◽

Qijun Zhao

Keyword(s):

Feature Learning ◽

Super Resolution ◽

Back Projection ◽

Single Image ◽

Image Super Resolution ◽

Single Image Super Resolution ◽

Projection Network

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Deep fractal residual network for fast and accurate single image super resolution

Neurocomputing ◽

10.1016/j.neucom.2019.09.093 ◽

2020 ◽

Vol 398 ◽

pp. 389-398 ◽

Cited By ~ 1

Author(s):

Yanghao Zhou ◽

Jianfeng Dong ◽

Yubin Yang

Keyword(s):

Super Resolution ◽

Single Image ◽

Residual Network ◽

Image Super Resolution ◽

Single Image Super Resolution

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Real-Time Environment Monitoring Using a Lightweight Image Super-Resolution Network

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18115890 ◽

2021 ◽

Vol 18 (11) ◽

pp. 5890

Author(s):

Qiang Yu ◽

Feiqiang Liu ◽

Long Xiao ◽

Zitao Liu ◽

Xiaomin Yang

Keyword(s):

Deep Learning ◽

Real Time ◽

Super Resolution ◽

Model Complexity ◽

Practical Application ◽

Single Image ◽

Feature Maps ◽

Benchmark Datasets ◽

Image Super Resolution ◽

Single Image Super Resolution

Deep-learning (DL)-based methods are of growing importance in the field of single image super-resolution (SISR). The practical application of these DL-based models is a remaining problem due to the requirement of heavy computation and huge storage resources. The powerful feature maps of hidden layers in convolutional neural networks (CNN) help the model learn useful information. However, there exists redundancy among feature maps, which can be further exploited. To address these issues, this paper proposes a lightweight efficient feature generating network (EFGN) for SISR by constructing the efficient feature generating block (EFGB). Specifically, the EFGB can conduct plain operations on the original features to produce more feature maps with parameters slightly increasing. With the help of these extra feature maps, the network can extract more useful information from low resolution (LR) images to reconstruct the desired high resolution (HR) images. Experiments conducted on the benchmark datasets demonstrate that the proposed EFGN can outperform other deep-learning based methods in most cases and possess relatively lower model complexity. Additionally, the running time measurement indicates the feasibility of real-time monitoring.

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