Results 31 to 40 of about 83,395 (347)
Quantum Annealing for Single Image Super-Resolution
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023 Accepted to IEEE/CVF CVPR 2023, NTIRE Challenge and Workshop.
Han Yao Choong +2 more
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Deep coordinate attention network for single image super‐resolution
IET Image Processing, 2022 Deep learning techniques and deep networks have recently been extensively studied and widely applied to single image super‐resolution (SR). Among them, channel attention has garnered the most focus owing to its significant boost in the presentational ...
Chao Xie, Hongyu Zhu, Yeqi Fei
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Super-Resolution Microscopy: A Virus’ Eye View of the Cell [PDF]
, 2014 It is difficult to observe the molecular choreography between viruses and host cell components, as they exist on a spatial scale beyond the reach of conventional microscopy.
Grove, J, Grove, Joe, Joe Grove
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Applied Sciences, 2020 The super-resolution generative adversarial network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied by unpleasant artifacts.
Zetao Jiang, Yongsong Huang, Lirui Hu
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Super Resolution for Noisy Images Using Convolutional Neural Networks
Mathematics, 2022 The images in high resolution contain more useful information than the images in low resolution. Thus, high-resolution digital images are preferred over low-resolution images.
Zaid Bin Mushtaq +5 more
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Volatile-Nonvolatile Memory Network for Progressive Image Super-Resolution
IEEE Access, 2021 Single-image super-resolution, i.e., reconstructing a high-resolution image from a low-resolution image, is a critical concern in many computer vision applications. Recent deep learning-based image super-resolution methods employ massive numbers of model
Jun-Ho Choi +3 more
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Single-Image Super-Resolution: A Benchmark [PDF]
, 2014 Single-image super-resolution is of great importance for vision applications, and numerous algorithms have been proposed in recent years. Despite the demonstrated success, these results are often generated based on different assumptions using different datasets and metrics.
Chih-Yuan Yang +2 more
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Clustering-oriented Multiple Convolutional Neural Networks for Single Image Super-resolution [PDF]
, 2017 This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record.In contrast to the human visual system (HVS) that applies different processing schemes to visual information of different textural ...
Ren, Peng +11 more
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Remote Sensing, 2021 Super-resolution (SR) technology has emerged as an effective tool for image analysis and interpretation. However, single hyperspectral (HS) image SR remains challenging, due to the high spectral dimensionality and lack of available high-resolution ...
Xiaochen Lu +3 more
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Terrain Self-Similarity-Based Transformer for Generating Super Resolution DEMs
Remote Sensing, 2023 High-resolution digital elevation models (DEMs) are important for relevant geoscience research and practical applications. Compared with traditional hardware-based methods, super-resolution (SR) reconstruction techniques are currently low-cost and ...
Xin Zheng, Zelun Bao, Qian Yin
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