Skip to main navigation
Skip to search
Skip to main content
Beijing Institute of Technology Home
English
中文
Home
Profiles
Research units
Research output
Prizes
Search by expertise, name or affiliation
View Scopus Profile
Jun Ke
School of Optics and Photonics
h-index
440
Citations
12
h-index
Calculated based on number of publications stored in Pure and citations from Scopus
2005
2024
Research activity per year
Overview
Fingerprint
Network
Research output
(84)
Fingerprint
Dive into the research topics where Jun Ke is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Engineering
Imaging Systems
100%
Compressed Sensing
54%
High Resolution
52%
Compressive Sensing
48%
Image Reconstruction
40%
Deep Learning
36%
Light Level
36%
Experimental Result
28%
Spatial Resolution
28%
Block Size
27%
Principal Components
27%
Fourier Transform
27%
Component Analysis
22%
Resolution Image
21%
Root-Mean-Squared Error
21%
Measurement Matrix
20%
Low Resolution Image
20%
Joints (Structural Components)
18%
Graphics Processing Unit
18%
Frame Object
18%
Optical Design
18%
Linear Combination
15%
Signal-to-Noise Ratio
14%
Detector Array
14%
Orthogonal Matching Pursuit
13%
Frame Rate
13%
Scattering Medium
13%
Design Matrix
13%
Sensing Method
13%
Metrics
12%
Light Source
12%
Pursuit Algorithm
12%
Super-Resolution Imaging
12%
Measurement System
11%
Image Analysis
11%
Signal Reconstruction
11%
Measurement Signal
11%
Dynamic Range
11%
Conventional Method
10%
Simulation Result
10%
Mean Square Error
10%
Limitations
10%
Convolutional Neural Network
9%
Dictionary Learning
9%
Analog Bandwidth
9%
Transmissions
9%
Mean-Squared-Error
9%
Measurement Resolution
9%
Color Coding
9%
Laser Wavelength
9%
Computer Science
Imaging Systems
58%
Image Reconstruction
40%
Compressed Sensing
39%
super resolution
36%
Spatial Resolution
31%
Experimental Result
30%
Lower Light Level
27%
Deep Learning
25%
Image Quality
22%
Scattering Medium
21%
Low Resolution Image
19%
Attention (Machine Learning)
18%
Deep Residual Network
18%
Reconstruction Error
18%
Neural Network
18%
Noise-to-Signal Ratio
17%
Systems Performance
13%
Decomposition Method
13%
Root Mean Squared Error
12%
Time Resolution
11%
Residual Neural Network
11%
Linear Combination
11%
Object Reconstruction
10%
Matrix Measurement
10%
Image Processing
10%
Dictionary Learning
9%
Good Performance
9%
Detector Array
9%
Collection Process
9%
Image Capture
9%
Reconstruction Problem
9%
reconstruction algorithm
9%
Signal Reconstruction
9%
Learning Approach
9%
Postprocessing
9%
Trained Network
9%
Error Estimation
9%
Temporal Correlation
9%
Spectral Imaging
9%
Principal Components
9%
Subsequent Processing
9%
Image Sensors
9%
Sampling Ratio
9%
Spectral Signature
9%
Super-Resolution Imaging
9%
Spatial Correlation
9%
Structured Light
9%
U-Net
9%
Computational Photography
9%
3D Convolutional Neural Networks
9%
Physics
Compressed Sensing
72%
High Resolution
47%
Deep Learning
45%
Line of Sight
27%
Neural Network
22%
Convolutional Neural Network
22%
Data Acquisition
21%
Signal-to-Noise Ratio
18%
Dynamic Range
18%
Near Infrared
18%
Laser Source
18%
Holography
18%
Optical Radar
18%
Laser Beams
14%
Gaussian Distribution
13%
Image Reconstruction
13%
Pulsed Laser
13%
Shape Function
9%
Random Noise
9%
Satellite Communication
9%
Image Analysis
9%
Optical Imaging
9%
Data Compression
9%
Bioimaging
9%
Continuous Wave Laser
9%
Noise Measurement
9%
Data Sampling
9%
Linewidth
9%
Optics
9%
Light Emitting Diode
9%
Sensor Network
9%
Photocathode
9%
Spectral Band
9%
Atmospheric Turbulence
9%
Image Classification
6%
Image Restoration
6%
