Results 281 to 290 of about 1,135,244 (321)

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

ECCV Workshops, 2018
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 with unpleasant artifacts.
Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Chen Change Loy, Y. Qiao, Xiaoou Tang   +8 more
semanticscholar   +1 more source

TTST: A Top-k Token Selective Transformer for Remote Sensing Image Super-Resolution

IEEE Transactions on Image Processing
Transformer-based method has demonstrated promising performance in image super-resolution tasks, due to its long-range and global aggregation capability. However, the existing Transformer brings two critical challenges for applying it in large-area earth
Yi Xiao, Qiangqiang Yuan, Kui Jiang, Jiang He, Chia-Wen Lin, Liangpei Zhang   +5 more
semanticscholar   +1 more source

Model-Informed Multistage Unsupervised Network for Hyperspectral Image Super-Resolution

IEEE Transactions on Geoscience and Remote Sensing
By fusing a low-resolution hyperspectral image (LrMSI) with an auxiliary high-resolution multispectral image (HrMSI), hyperspectral image super-resolution (HISR) can generate a high-resolution hyperspectral image (HrHSI) economically.
Jiaxin Li, Ke Zheng, Lianru Gao, Li Ni, Min Huang, Jocelyn Chanussot   +5 more
semanticscholar   +1 more source

Super resolution image reconstruction and imaging device

2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2016
Super resolution (SR) studies started in the 1990s and many papers were issued in the 2000s. Super resolution image reconstruction (SRR) is one of the most common SR methods. SRR reconstructs a high-resolution image (HRI) using multiple low-resolution images (LRIs).
Chinatsu Mori, Kenkichi Tanioka, Seiichi Gohshi   +2 more
openaire   +1 more source

Image Fusion for Hyperspectral Image Super-Resolution

2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2018
Hyperspectral sensors have high spectral resolution by capturing images in hundreds of bands. Despite the high spectral resolution, low spatial resolution of these sensors restricts the performance of the hyperspectral imaging applications such as target tracking and image classification.
Hasan Irmak, Gozde Bozdagi Akar, Seniha Esen Yüksel   +2 more
openaire   +1 more source

Super-resolution reconstruction of hyperspectral images

IEEE Transactions on Image Processing, 2005
Hyperspectral images are used for aerial and space imagery applications, including target detection, tracking, agricultural, and natural resource exploration. Unfortunately, atmospheric scattering, secondary illumination, changing viewing angles, and sensor noise degrade the quality of these images.
Toygar Akgun, Yucel Altunbasak, Russell M. Mersereau   +2 more
openaire   +2 more sources

Image GPT with Super Resolution

2022
Bhumika Shah, Ankita Sinha, Prashant Saxena   +2 more
openaire   +1 more source

Regularization for Super-Resolution Image Reconstruction

2006
Super-resolution image reconstruction estimates a high-resolution image from a sequence of low-resolution, aliased images. The estimation is an inverse problem and is known to be ill-conditioned, in the sense that small errors in the observed images can cause large changes in the reconstruction.
openaire   +2 more sources

Learning a Deep Convolutional Network for Image Super-Resolution

European Conference on Computer Vision, 2014
Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang   +3 more
semanticscholar   +1 more source

NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017
E. Agustsson, R. Timofte
semanticscholar   +1 more source