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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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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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Jie Gao, Yiping Cao, Jin Chen and Xiuzhang Huang
When the measured object is fast moving online, the captured deformed pattern may appear as motion blur, and some phase information will be lost. Therefore, the frame rate has to be improved by adjusting the image acquisition mode of the camera to adapt ...
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Haoran Xu, Xinya Li, Kaiyi Zhang, Yanbai He, Haoran Fan, Sijiang Liu, Chuanyan Hao and Bo Jiang
Recently, deep learning has enabled a huge leap forward in image inpainting. However, due to the memory and computational limitation, most existing methods are able to handle only low-resolution inputs, typically less than 1 K. With the improvement of In...
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Lei Yu, Xuewei Zhang and Yan Chu
In this paper, an adaptive dual-regularization super-resolution reconstruction algorithm based on sub-pixel convolution (MPSR) is proposed. There are two novel features of the algorithm: First, the traditional regularization algorithm and sub-pixel convo...
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Zhihang Liu, Pengfei He and Feifei Wang
Image super-resolution reconstruction technology can boost image resolution and aid in the discovery of PCB flaws. The traditional SRGAN algorithm produces reconstructed images with great realism, but it also has the disadvantages of insufficient feature...
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Krzysztof Malczewski
One of the most challenging aspects of medical modalities such as Computed Tomography (CT) as well hybrid techniques such as CT/PET (Computed Tomography/Positron emission tomography) and PET/MRI is finding a balance between examination time, radiation do...
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Kai Liu, Xiao Yu, Yongsen Xu, Yulei Xu, Yuan Yao, Nan Di, Yefei Wang, Hao Wang and Honghai Shen
Diffractive optical elements (DOEs) are difficult to apply in natural scenes imaging covering the visible bandwidth-spectral due to their strong chromatic aberration and the decrease in diffraction efficiency. Advances in computational imaging make it po...
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Jingru Hou, Yujuan Si and Xiaoqian Yu
Application areas of image super-resolution: surveillance, medical diagnosis, Earth observation and remote sensing, astronomical observation, biometric information identification.
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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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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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Yuantao Chen, Jin Wang, Xi Chen, Arun Kumar Sangaiah, Kai Yang and Zhouhong Cao
For the image super-resolution method from a single channel, it is difficult to achieve both fast convergence and high-quality texture restoration. By mitigating the weaknesses of existing methods, the present paper proposes an image super-resolution alg...
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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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Shanshan Liu, Qingbin Huang and Minghui Wang
Multi-frame super-resolution makes up for the deficiency of sensor hardware and significantly improves image resolution by using the information of inter-frame and intra-frame images. Inaccurate blur kernel estimation will enlarge the distortion of the e...
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Xiaofeng Du and Yifan He
Super-resolution (SR) technology is essential for improving image quality in magnetic resonance imaging (MRI). The main challenge of MRI SR is to reconstruct high-frequency (HR) details from a low-resolution (LR) image. To address this challenge, we deve...
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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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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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Yuanzhou Zheng, Peng Liu, Long Qian, Shiquan Qin, Xinyu Liu, Yong Ma and Ganjun Cheng
To improve the navigation safety of inland river ships and enrich the methods of environmental perception, this paper studies the recognition and depth estimation of inland river ships based on binocular stereo vision (BSV). In the stage of ship recognit...
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Sampada Tavse, Vijayakumar Varadarajan, Mrinal Bachute, Shilpa Gite and Ketan Kotecha
With the advances in brain imaging, magnetic resonance imaging (MRI) is evolving as a popular radiological tool in clinical diagnosis. Deep learning (DL) methods can detect abnormalities in brain images without an extensive manual feature extraction proc...
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