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Rong Wang, Yonghui Zhang and Yulu Zhang
The absorption and scattering of light in water usually result in the degradation of underwater image quality, such as color distortion and low contrast. Additionally, the performance of acquisition devices may limit the spatial resolution of underwater ...
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Hexin Lu, Xiaodong Zhu, Jingwei Cui and Haifeng Jiang
The process of iris recognition can result in a decline in recognition performance when the resolution of the iris images is insufficient. In this study, a super-resolution model for iris images, namely SwinGIris, which combines the Swin Transformer and ...
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Yimin Ma, Yi Xu, Yunqing Liu, Fei Yan, Qiong Zhang, Qi Li and Quanyang Liu
In recent years, deep convolutional neural networks with multi-scale features have been widely used in image super-resolution reconstruction (ISR), and the quality of the generated images has been significantly improved compared with traditional methods....
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Jiaming Bian, Ye Liu and Jun Chen
In recent times, remote sensing image super-resolution reconstruction technology based on deep learning has experienced rapid development. However, most algorithms in this domain concentrate solely on enhancing the super-resolution network?s performance ...
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Jian Liu, Shihui Yu, Xuemei Liu, Guohang Lu, Zhenbo Xin and Jin Yuan
In-field in situ droplet deposition digitization is beneficial for obtaining feedback on spraying performance and precise spray control, the cost-effectiveness of the measurement system is crucial to its scalable application. However, the limitations of ...
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Moiz Hassan, Kandasamy Illanko and Xavier N. Fernando
Single Image Super Resolution (SSIR) is an intriguing research topic in computer vision where the goal is to create high-resolution images from low-resolution ones using innovative techniques. SSIR has numerous applications in fields such as medical/sate...
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Yaru Zhang, Jiantao Liu, Tong Zhang and Zhibiao Zhao
In the process of stereo super-resolution reconstruction, in addition to the richness of the extracted feature information directly affecting the texture details of the reconstructed image, the texture details of the corresponding pixels between stereo i...
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Asif Hussain Khan, Christian Micheloni and Niki Martinel
Blind image super-resolution (Blind-SR) is the process of leveraging a low-resolution (LR) image, with unknown degradation, to generate its high-resolution (HR) version. Most of the existing blind SR techniques use a degradation estimator network to expl...
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Honghao Li, Xiran Zhou and Zhigang Yan
The purpose of multisource map super-resolution is to reconstruct high-resolution maps based on low-resolution maps, which is valuable for content-based map tasks such as map recognition and classification. However, there is no specific super-resolution ...
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Valdivino Alexandre de Santiago Júnior
Despite several solutions and experiments have been conducted recently addressing image super-resolution (SR), boosted by deep learning (DL), they do not usually design evaluations with high scaling factors. Moreover, the datasets are generally benchmark...
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