Results 61 to 70 of about 750,933 (209)

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, She Kun, Fayaz Ali Dharejo, Yuanchun Zhou   +3 more
doaj   +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, Huiyu Zeng, Hongwei Yong, Zisheng Cao, Lei Zhang   +4 more
semanticscholar   +1 more source

Super-Resolution Imaging Spectroscopy [PDF]

Applied Spectroscopy, 1994
Among the remaining challenges facing chemical analysis is the capability of extending to micro-scale samples the spectacular detection limits that have been achieved for a variety of spectroscopic methods. In such samples, a vanishingly small quantity of target analyte material, relative to background, presents a serious obstacle to achieving the rate
T. D. Harris, R. D. Grober, J. K. Trautman, E. Betzig   +3 more
openaire   +1 more source

Quantitative Assessment of Single-Image Super-Resolution in Myocardial Scar Imaging

IEEE Journal of Translational Engineering in Health and Medicine, 2014
Single-image super resolution is a process of obtaining a high-resolution image from a set of low-resolution observations by signal processing. While super resolution has been demonstrated to improve image quality in scaled down images in the image ...
Hiroshi Ashikaga, Heidi L. Estner, Daniel A. Herzka, Elliot R. Mcveigh, Henry R. Halperin   +4 more
doaj   +1 more source

Depth Map Super-Resolution via Cascaded Transformers Guidance

Frontiers in Signal Processing, 2022
Depth information captured by affordable depth sensors is characterized by low spatial resolution, which limits potential applications. Several methods have recently been proposed for guided super-resolution of depth maps using convolutional neural ...
Ido Ariav, Israel Cohen
doaj   +1 more source

Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform [PDF]

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018
Despite that convolutional neural networks (CNN) have recently demonstrated high-quality reconstruction for single-image super-resolution (SR), recovering natural and realistic texture remains a challenging problem.
Xintao Wang, K. Yu, Chao Dong, Chen Change Loy   +3 more
semanticscholar   +1 more source

Application of Distributed Parallel Computing in Super-Resolution Image Enbancement

Dianxin kexue, 2015
The distributed parallel computing in the super-resolution image enhancement applications was introduced.Super resolution image enhancement refers to improving the image resolution of 1080P video to 4K by software without upgrading existing acquisition ...
Jie Zheng, Sheng Bao, Ying Yang
doaj   +2 more sources

A plexus‐convolutional neural network framework for fast remote sensing image super‐resolution in wavelet domain

IET Image Processing, 2021
Satellite image processing has been widely used in recent years in a number of applications such as land classification, Identification transfer, resource exploration, super‐resolution image, etc.
Farah Deeba, Yuanchun Zhou, Fayaz Ali Dharejo, Muhammad Ashfaq Khan, Bhagwan Das, Xuezhi Wang, Yi Du   +6 more
doaj   +1 more source

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
doaj   +1 more source

NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017
This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results. A new DIVerse 2K resolution image dataset (DIV2K) was employed.
R. Timofte, E. Agustsson, L. Gool, Ming-Hsuan Yang, Lei Zhang, Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, Kyoung Mu Lee, Xintao Wang, Yapeng Tian, K. Yu, Yulun Zhang, Shixiang Wu, Chao Dong, Liang Lin, Y. Qiao, Chen Change Loy, Woong Bae, J. Yoo, Yoseob Han, J. C. Ye, Jae-Seok Choi, Munchurl Kim, Yuchen Fan, Jiahui Yu, Wei Han, Ding Liu, Haichao Yu, Zhangyang Wang, Humphrey Shi, Xinchao Wang, Thomas S. Huang, Yunjin Chen, K. Zhang, W. Zuo, Zhimin Tang, Linkai Luo, Shaohui Li, Min Fu, Lei Cao, Wen Heng, Giang Bui, Truc Le, Y. Duan, D. Tao, Ruxin Wang, Xu Lin, Jianxin Pang, Jinchang Xu, Yu Zhao, Xiangyu Xu, Jin-shan Pan, Deqing Sun, Yujin Zhang, Xibin Song, Yuchao Dai, Xueying Qin, X. Huynh, Tiantong Guo, Hojjat Seyed Mousavi, T. Vu, V. Monga, Cristóvão Cruz, K. Egiazarian, V. Katkovnik, Rakesh Mehta, A. Jain, Abhinav Agarwalla, C. Praveen, Ruofan Zhou, Hongdiao Wen, Chen Zhu, Zhiqiang Xia, Zhengtao Wang, Qi Guo   +76 more
semanticscholar   +1 more source