Results 201 to 210 of about 12,335,378 (248)

Early Clinical and Cerebrospinal Fluid Predictors of 1‐Year Recurrence in Autoimmune GFAP Astrocytopathy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Autoimmune glial fibrillary acidic protein astrocytopathy (GFAP‐A) is an inflammatory central nervous system disorder with variable outcomes. Relapse occurs in a subset of patients, but early predictors remain unclear. We aimed to identify admission‐available features associated with 1‐year recurrence and develop an interpretable ...
Qingting Hong, Yuping Xiao, Yuhan Wu, Ziyuan Huang, Weishan Liang, Li Xiao, Yaqing Shu, Tingting Lu, Yanyu Chang, Wei Qiu, Yuge Wang   +10 more
wiley   +1 more source

Copine‐4 Paraneoplastic Autoimmune Encephalitis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT We characterize copine‐4 (CPNE4)‐IgG and report clinical associations. Among 844 samples with unclassified neural IgGs identified by mouse‐brain indirect immunofluorescence assay over 2 years, 5 samples (serum, 3; CSF, 2) from 3 patients produced a distinctive cerebrum‐restricted synaptic pattern.
Mimi S. Fang, Amy Ml Quek, Clement Yong, Amir Adeli, Gabrielle N. Conway, Connie E. Lesnick, Diana Anissian, Divyanshu Dubey, John R. Mills, Sean J. Pittock, Anastasia Zekeridou, Michael Gilligan, Andrew McKeon   +12 more
wiley   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Low Light Enhancement by Unsupervised Network

2020 IEEE International Conference on Real-time Computing and Robotics (RCAR), 2020
This paper focuses on unsupervised low-light enhancement methods for practical applications such as automated driving, surveillance cameras, and so on. This paper proposes an unsupervised method that is independent of paired samples and works well to solve the image darkness problem.
Yangyang Qu, Yongsheng Ou, Rong Xiong
openaire   +1 more source

Benchmarking Low-Light Image Enhancement and Beyond

International Journal of Computer Vision, 2021
In this paper, we present a systematic review and evaluation of existing single-image low-light enhancement algorithms. Besides the commonly used low-level vision oriented evaluations, we additionally consider measuring machine vision performance in the low-light condition via face detection task to explore the potential of joint optimization of high ...
Jiaying Liu, Wenhan Yang, Dejia Xu
exaly   +3 more sources

Aesthetics-Guided Low-Light Enhancement

IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluating the performance of low-light image enhancement (LLE) is highly subjective, thus making integrating human preferences into LLE a necessity. Existing methods fail to consider this and present a series of potentially valid heuristic criteria for training LLE models.
Dong Liang 0008, Yuanhang Gao, Ling Li 0010, Zhengyan Xu, Sheng-Jun Huang, Songcan Chen   +5 more
openaire   +2 more sources

Lightening Network for Low-Light Image Enhancement

IEEE Transactions on Image Processing, 2020
Low-light image enhancement is a challenging task that has attracted considerable attention. Pictures taken in low-light conditions often have bad visual quality. To address the problem, we regard the low-light enhancement as a residual learning problem that is to estimate the residual between low- and normal-light images.
Li-Wen Wang, Zhi-Song Liu, Wan-Chi Siu, Daniel Pak-Kong Lun   +3 more
openaire   +2 more sources

Polarization-Aware Low-Light Image Enhancement

Proceedings of the AAAI Conference on Artificial Intelligence, 2023
Polarization-based vision algorithms have found uses in various applications since polarization provides additional physical constraints. However, in low-light conditions, their performance would be severely degenerated since the captured polarized images could be noisy, leading to noticeable degradation in the degree of polarization (DoP) and the ...
Chu Zhou, Minggui Teng, Youwei Lyu, Si Li 0001, Chao Xu 0006, Boxin Shi   +5 more
openaire   +1 more source

Image enhancement for extremely low light conditions

2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2014
In this paper, a novel methodology is proposed for contrast enhancement and noise reduction in very noisy data with low dynamic range on images captured by surveillance camera under extremely low light condition. For the initial noise reduction, a motion adaptive temporal filtering based on the Kalman filter is employed.
Dubok Park, Minjae Kim, Bonhwa Ku, Sangmin Yoon, David K. Han   +4 more
openaire   +2 more sources

Semantically-guided low-light image enhancement

Pattern Recognition Letters, 2020
Abstract Recently, extensive research efforts have been made on low-light image enhancement. Many novel models have been proposed, such as the ones based on the Retinex theory, multiple exposure fusion, and deep neural networks. However, current models do not directly consider the semantic information in the modeling process.
Junyi Xie, Hao Bian, Yuanhang Wu, Yu Zhao, Linmin Shan, Shijie Hao   +5 more
openaire   +2 more sources

A novel framework for enhancement of the low lighting video

2017 IEEE Symposium on Computers and Communications (ISCC), 2017
A novel and effective framework for the enhancement of low lighting images is proposed in this paper. The novel framework presents an optimized de-haze algorithm on inverted images to enhance the low-dynamic-range images which optimizes the complicated process of computing the parameters A and t(x).
Jianhua Pang, Sheng Zhang 0003, Wencang Bai   +2 more
openaire   +1 more source