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Epitomic Image Super-Resolution
Proceedings of the AAAI Conference on Artificial Intelligence, 2016We propose Epitomic Image Super-Resolution (ESR) to enhance the current internal SR methods that exploit the self-similarities in the input. Instead of local nearest neighbor patch matching used in most existing internal SR methods, ESR employs epitomic patch matching that features robustness to noise, and both local and non-local patch
Yingzhen Yang +6 more
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A survey on super-resolution imaging
Signal, Image and Video Processing, 2011The key objective of super-resolution (SR) imaging is to reconstruct a higher-resolution image based on a set of images, acquired from the same scene and denoted as ‘low-resolution’ images, to overcome the limitation and/or ill-posed conditions of the image acquisition process for facilitating better content visualization and scene recognition. In this
Jing Tian 0002, Kai-Kuang Ma
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Image Super-Resolution with Non-Local Sparse Attention
Computer Vision and Pattern Recognition, 2021Both Non-Local (NL) operation and sparse representation are crucial for Single Image Super-Resolution (SISR). In this paper, we investigate their combinations and propose a novel Non-Local Sparse Attention (NLSA) with dynamic sparse attention pattern ...
Yiqun Mei, Yuchen Fan, Yuqian Zhou
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Super-Resolution in Medical Imaging
The Computer Journal, 2008This paper provides an overview on super-resolution (SR) research in medical imaging applications. Many imaging modalities exist. Some provide anatomical information and reveal information about the structure of the human body, and others provide functional information, locations of activity for specific activities and specified tasks.
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Dual Aggregation Transformer for Image Super-Resolution
IEEE International Conference on Computer Vision, 2023Transformer has recently gained considerable popularity in low-level vision tasks, including image super-resolution (SR). These networks utilize self-attention along different dimensions, spatial or channel, and achieve impressive performance.
Zheng Chen +5 more
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Bayesian Methods for Image Super-Resolution
The Computer Journal, 2008We present a novel method of Bayesian image super-resolution in which marginalization is carried out over latent parameters such as geometric and photometric registration and the image point-spread function. Related Bayesian super-resolution approaches marginalize over the high-resolution image, necessitating the use of an unfavourable image prior ...
Pickup, LC +3 more
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Image and Vision Computing, 2006
Abstract The shortcomings in commonly used kernel-based super-resolution drive the study of improved super-resolution algorithms of higher quality. In the past years a wide range of very different approaches has been taken to improve super-resolution.
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Abstract The shortcomings in commonly used kernel-based super-resolution drive the study of improved super-resolution algorithms of higher quality. In the past years a wide range of very different approaches has been taken to improve super-resolution.
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One-Step Effective Diffusion Network for Real-World Image Super-Resolution
Neural Information Processing SystemsThe pre-trained text-to-image diffusion models have been increasingly employed to tackle the real-world image super-resolution (Real-ISR) problem due to their powerful generative image priors.
Rongyuan Wu +3 more
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Enhanced Deep Image Prior for Unsupervised Hyperspectral Image Super-Resolution
IEEE Transactions on Geoscience and Remote SensingDepending on a large-scale paired dataset of low-resolution hyperspectral image (LrHSI), high-resolution multispectral image (HrMSI), and corresponding high-resolution hyperspectral image (HrHSI), the supervised paradigm has achieved impressive ...
Jiaxin Li +5 more
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Residual Feature Distillation Network for Lightweight Image Super-Resolution
ECCV Workshops, 2020Recent advances in single image super-resolution (SISR) explored the power of convolutional neural network (CNN) to achieve a better performance. Despite the great success of CNN-based methods, it is not easy to apply these methods to edge devices due to
Jie Liu, Jie Tang, Gangshan Wu
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