Results 31 to 40 of about 389,857 (275)
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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Pairwise Operator Learning for Patch Based Single-image Super-resolution [PDF]
, 2016 Motivated by the fact that image patches could be inherently represented by matrices, single-image super-resolution is treated as a problem of learning regression operators in a matrix space in this paper. The regression operators that map low-resolution
Shao, Ling, Tang, Yi
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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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Edge-Informed Single Image Super-Resolution [PDF]
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019 The recent increase in the extensive use of digital imaging technologies has brought with it a simultaneous demand for higher-resolution images. We develop a novel edge-informed approach to single image super-resolution (SISR). The SISR problem is reformulated as an image inpainting task.
Nazeri, Kamyar +2 more
openaire +2 more sources
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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Super-resolution from a single image [PDF]
2009 IEEE 12th International Conference on Computer Vision, 2009 Methods for super-resolution can be broadly classified into two families of methods: (i) The classical multi-image super-resolution (combining images obtained at subpixel misalignments), and (ii) Example-Based super-resolution (learning correspondence between low and high resolution image patches from a database).
Daniel Glasner, Shai Bagon, Michal Irani
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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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Transformer for Single Image Super-Resolution
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022 Accepted by CVPR workshop ...
Lu, Zhisheng +5 more
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Deep Learning-Based Single-Image Super-Resolution: A Comprehensive Review
IEEE Access, 2023 High-fidelity information, such as 4K quality videos and photographs, is increasing as high-speed internet access becomes more widespread and less expensive.
Karansingh Chauhan +7 more
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Deep Learning for Single Image Super-Resolution: A Brief Review [PDF]
, 2019 Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions.
Liao, Q +5 more
core +2 more sources