Results 11 to 20 of about 10,359,966 (377)
Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT. [PDF]
J Digit Imaging, 2018 Umehara K, Ota J, Ishida T.
europepmc +2 more sources
Activating More Pixels in Image Super-Resolution Transformer [PDF]
Computer Vision and Pattern Recognition, 2022 Transformer-based methods have shown impressive performance in low-level vision tasks, such as image super-resolution. However, we find that these networks can only utilize a limited spatial range of input information through attribution analysis.
Xiangyu Chen +3 more
semanticscholar +1 more source
ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting [PDF]
Neural Information Processing Systems, 2023 Diffusion-based image super-resolution (SR) methods are mainly limited by the low inference speed due to the requirements of hundreds or even thousands of sampling steps.
Zongsheng Yue +2 more
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Taming Transformers for High-Resolution Image Synthesis [PDF]
Computer Vision and Pattern Recognition, 2020 Designed to learn long-range interactions on sequential data, transformers continue to show state-of-the-art results on a wide variety of tasks. In contrast to CNNs, they contain no inductive bias that prioritizes local interactions.
Patrick Esser, Robin Rombach, B. Ommer
semanticscholar +1 more source
IET Image Processing, 2023 With constant advances in deep learning methods as applied to image processing, deep convolutional neural networks (CNNs) have been widely explored in single‐image super‐resolution (SISR) problems and have attained significant success.
Yuanyuan Liu +3 more
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Enhanced Dense Space Attention Network for Super-Resolution Construction From Single Input Image
IEEE Access, 2021 In some applications, such as surveillance and biometrics, image enlargement is required to inspect small details on the image. One of the image enlargement approaches is by using convolutional neural network (CNN)-based super-resolution construction ...
Yoong Khang Ooi +2 more
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High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs).
Ting-Chun Wang +5 more
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Enhanced Deep Residual Networks for Single Image Super-Resolution [PDF]
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved performance.
Bee Lim +4 more
semanticscholar +1 more source
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network [PDF]
Computer Vision and Pattern Recognition, 2016 Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved: how do we recover the finer texture details when we super-resolve at ...
C. Ledig +8 more
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Efficient Long-Range Attention Network for Image Super-resolution [PDF]
European Conference on Computer Vision, 2022 Recently, transformer-based methods have demonstrated impressive results in various vision tasks, including image super-resolution (SR), by exploiting the self-attention (SA) for feature extraction.
Xindong Zhang +3 more
semanticscholar +1 more source