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基于像素暗噪声估计的 EBAPS 图像自适应小波阈值降噪
Xuan Liu, Bingzhen Li,
Li Li
*
,
Weiqi Jin
, Hongchang Cheng
*
此作品的通讯作者
光电学院
Beijing Institute of Technology
Science and Technology on Low-Light-Level Night Vision Laboratory
科研成果
:
期刊稿件
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文章
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同行评审
1
引用 （Scopus）
综述
指纹
指纹
探究 '基于像素暗噪声估计的 EBAPS 图像自适应小波阈值降噪' 的科研主题。它们共同构成独一无二的指纹。
分类
加权
按字母排序
Engineering
Intensity Noise
100%
Signal Intensity
83%
Signal Strength
66%
Illuminance
66%
Additive White Gaussian Noise
66%
Noise Source
50%
Sensor Signal
33%
Fixed Pattern
33%
Image Sensor
33%
Image Noise
33%
Sensor Noise
33%
Gaussian White Noise
16%
Image Processing
16%
Noise Variance
16%
Signal-to-Noise Ratio
16%
Gaussians
16%
Noise Level
16%
Total Variation
16%
Early Stage
16%
Max
16%
Pixel Value
16%
Histogram
16%
Burrs
16%
Denoised Image
16%
Noise Pattern
16%
Shot Noise
16%
Computer Science
de-noising
100%
Signal Intensity
45%
Signal Strength
36%
Additive White Gaussian Noise
36%
Pixel Structure
27%
wavelet denoising
18%
Image Noise
18%
Image Sensors
18%
Noise-to-Signal Ratio
9%
Estimation Method
9%
Noise Variance
9%
Imaging Process
9%
Image Processing
9%
Gaussian White Noise
9%
Image Quality
9%
Detail Information
9%
Wavelet Transforms
9%
Imaging Sensor
9%
Total Variation
9%
Lower Performance
9%
Physics
Wavelet
100%
Noise Intensity
63%
Random Noise
45%
Image Sensor
18%
Image Processing
9%
Signal-to-Noise Ratio
9%
Gaussian Distribution
9%
Wavelet Analysis
9%
Noise Reduction
9%