Results 61 to 70 of about 1,135,244 (321)
Learning Texture Transformer Network for Image Super-Resolution [PDF]
Computer Vision and Pattern Recognition, 2020 We study on image super-resolution (SR), which aims to recover realistic textures from a low-resolution (LR) image. Recent progress has been made by taking high-resolution images as references (Ref), so that relevant textures can be transferred to LR ...
Fuzhi Yang +4 more
semanticscholar +1 more source
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
doaj +1 more source
Transformer for Single Image Super-Resolution [PDF]
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021 Single image super-resolution (SISR) has witnessed great strides with the development of deep learning. However, most existing studies focus on building more complex networks with a massive number of layers.
Zhisheng Lu +5 more
semanticscholar +1 more source
Joint Image Reconstruction and Super-Resolution for Accelerated Magnetic Resonance Imaging
Bioengineering, 2023 Magnetic resonance (MR) image reconstruction and super-resolution are two prominent techniques to restore high-quality images from undersampled or low-resolution k-space data to accelerate MR imaging. Combining undersampled and low-resolution acquisition
Wei Xu +6 more
doaj +1 more source
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
, 2017 Face Super-Resolution (SR) is a domain-specific super-resolution problem. The specific facial prior knowledge could be leveraged for better super-resolving face images.
Chen, Yu +4 more
core +1 more source
Wavelet-Based Enhanced Medical Image Super Resolution
IEEE Access, 2020 Low-resolution medical images can seriously interfere with the medical diagnosis, and poor image quality can lead to loss of detailed information. Therefore, improving the quality of medical images and accelerating the reconstruction is of particular ...
Farah Deeba +3 more
doaj +1 more source
Fast and Accurate Single Image Super-Resolution via Information Distillation Network [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Recently, deep convolutional neural networks (CNNs) have been demonstrated remarkable progress on single image super-resolution. However, as the depth and width of the networks increase, CNN-based super-resolution methods have been faced with the ...
Zheng Hui, Xiumei Wang, Xinbo Gao
semanticscholar +1 more source
Guaranteed reconstruction for image super-resolution [PDF]
2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2013 This paper presents a new reconstruction operator to be used in a super-resolution scheme. Here, by reconstruction in super-resolution, we mean the back-projection operation, i.e. the way K low resolution (LR) images are aggregated to obtain a smooth high resolution (HR) image.
Graba, Farès +3 more
openaire +1 more source
Self Super-Resolution for Magnetic Resonance Images using Deep Networks
, 2018 High resolution magnetic resonance~(MR) imaging~(MRI) is desirable in many clinical applications, however, there is a trade-off between resolution, speed of acquisition, and noise.
Carass, Aaron +3 more
core +1 more source
Toward Real-World Single Image Super-Resolution: A New Benchmark and a New Model [PDF]
IEEE International Conference on Computer Vision, 2019 Most of the existing learning-based single image super-resolution (SISR) methods are trained and evaluated on simulated datasets, where the low-resolution (LR) images are generated by applying a simple and uniform degradation (i.e., bicubic downsampling)
Jianrui Cai +4 more
semanticscholar +1 more source