Image Super-Resolution Reconstruction Based on the Lightweight Hybrid Attention Network by Chu Yuezhong, Wang Kang, Zhang Xuefeng, Liu Heng

“…In order to solve the problem that the current image super-resolution model has too many parameters and high computational complexity, this paper proposes a lightweight hybrid attention network (LHAN). …”
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Image Super-Resolution Using Lightweight Multiscale Residual Dense Network by Shilin Li, Ming Zhao, Zhengyun Fang, Yafei Zhang, Hongjie Li

“…The current super-resolution methods cannot fully exploit the global and local information of the original low-resolution image, resulting in loss of some information. …”
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Feature enhanced cascading attention network for lightweight image super-resolution by Feng Huang, Hongwei Liu, Liqiong Chen, Ying Shen, Min Yu

Subjects: “…Lightweight image super-resolution…”
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Gradient pooling distillation network for lightweight single image super-resolution reconstruction by Zhiyong Hong, GuanJie Liang, Liping Xiong

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MFCEN: A lightweight multi-scale feature cooperative enhancement network for single-image super-resolution by Jiange Liu, Yu Chen, Xin Dai, Li Cao, Qingwu Li

Subjects: “…single-image super-resolution…”
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A Lightweight CNN-Transformer Implemented via Structural Re-Parameterization and Hybrid Attention for Remote Sensing Image Super-Resolution by Jie Wang, Hongwei Li, Yifan Li, Zilong Qin

Subjects: “…super-resolution…”
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Assessing the Effect of Super Plasticizer on Scoria Concrete. by Turyasiima, Ronald

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Residual trio feature network for efficient super-resolution by Junfeng Chen, Mao Mao, Azhu Guan, Altangerel Ayush

“…Abstract Deep learning-based approaches have demonstrated impressive performance in single-image super-resolution (SISR). Efficient super-resolution compromises the reconstructed image’s quality to have fewer parameters and Flops. …”
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Efficient Image Super-Resolution with Multi-Branch Mixer Transformer by Long Zhang, Yi Wan

“… Deep learning methods have demonstrated significant advancements in single image super-resolution (SISR), with Transformer-based models frequently outperforming CNN-based counterparts in performance. …”
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ViT-ISRGAN: A High-Quality Super-Resolution Reconstruction Method for Multispectral Remote Sensing Images by Yifeng Yang, Hengqian Zhao, Xiadan Huangfu, Zihan Li, Pan Wang

“…This model is an improvement upon the original SRGAN super-resolution image reconstruction method, incorporating lightweight network modules, channel attention modules, spatial-spectral residual attention, and the vision transformer structure. …”
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A novel transmission-augmented deep unfolding network with consideration of residual recovery by Zhijie Zhang, Huang Bai, Ljubiša Stanković, Junmei Sun, Xiumei Li

“…Furthermore, noting the difference between the original image and the output of SuperTA-Net, the reinforcement network is developed, where the main component called residual recovery network (RR-Net) is lightweight and can be added to reinforce all kinds of CS reconstruction networks. …”
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Generative Adversarial Networks for Unmanned Aerial Vehicle Object Detection with Fusion Technology by Nageswara Guptha M, Y. K. Guruprasad, Yuvaraja Teekaraman, Ramya Kuppusamy, Amruth Ramesh Thelkar

“…Its generator, in particular, learns to turn unsatisfactory tiny object representations into super-resolved items that are similar to large objects to deceive a rival discriminator. …”
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