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武汉大学 [2]
长春光学精密机械与物... [1]
武汉理工大学 [1]
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L1-L1 NORMS FOR FACE SUPER-RESOLUTION WITH MIXED GAUSSIAN-IMPULSE NOISE
会议论文
作者:
Jiang, Junjun
;
Wang, Zhongyuan
;
Chen, Chen
;
Lu, Tao
收藏
  |  
浏览/下载:8/0
  |  
提交时间:2019/12/05
Video surveillance
super-resolution
l(1)-l(1) norms
mixed Gaussian-impulse noise
sparse representation
Non-Local Regularized Variational Model for Image Deblurring under Mixed Gaussian-Impulse Noise
期刊论文
JOURNAL OF INTERNET TECHNOLOGY, 2015, 卷号: 16, 期号: 7, 页码: 1301-1319
作者:
Zhang, Chenyang
;
Wu, Di
;
Liu, Ryan Wen*
;
Xiong, Naixue
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  |  
浏览/下载:6/0
  |  
提交时间:2019/12/04
Image restoration
Image deblurring
Mixed Gaussian-impulse noise
Non-local total variation
Non-Local Regularized Variational Model for Image Deblurring under Mixed Gaussian-Impulse Noise
期刊论文
JOURNAL OF INTERNET TECHNOLOGY, 2015, 卷号: 16, 期号: 7
作者:
Zhang, Chenyang
;
Wu, Di
;
Liu, Ryan Wen
;
Xiong, Naixue
收藏
  |  
浏览/下载:2/0
  |  
提交时间:2019/12/05
Image restoration
Image deblurring
Mixed Gaussian-impulse noise
Non-local total variation
A new approach for the removal of mixed noise based on wavelet transform (EI CONFERENCE)
会议论文
ICO20: Remote Sensing and Infrared Devices and Systems, August 21, 2005 - August 26, 2005, Changchun, China
Li Y.
;
Ni H.
;
Pang W.
;
Hao Z.
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浏览/下载:27/0
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提交时间:2013/03/25
This paper proposed a new approach for the removal of mixed noise. There are many different ways in image denoising. Donoho et al have proposed a method for image de-noising by thresholding
ambiguity is often resulted in determining the correspondence of a modulus maximum to a singularity. In the light
and indeed
we combine the merits of the two techniques to form a new approach for the removal of mixed noise. At first
the application of their method to image denoising has been extremely successful. But the method of Donoho is based on the assumption that the type of noise is only additive Gaussian noise
we used wavelet singularity detection (WSD) technique to analyze singularities of signal and noise. According to the characteristic that wavelet transform modulus maxima of impulse noise rapidly decreases as the scale increases in wavelet domain
which is not successful for impulse noise. Mallat has also presented a method for signal denoising by discriminating the noise and the signal singularities through an analysis of their wavelet transform modulus maxima (WTMM). Nevertheless
it can be accurately located with multiscale space by going through dyadic orthogonal wavelet transform and removed. Furthermore the Gaussian noise is also removed through a level-dependent thresholding algorithm
the tracing of WTMM is not just tedious procedure computationally
algorithm. The experimental results demonstrate that the proposed method can effectively detect impulse noise and remove almost all of the noise while preserve image details very well.
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