scholarly journals Tight frame approximation for multi-frames and super-frames

2004 ◽  
Vol 129 (1) ◽  
pp. 78-93 ◽  
Author(s):  
Deguang Han
Keyword(s):  
Tight Frame

scholarly journals Compressive sensing MR imaging based on adaptive tight frame and reference image

2020 ◽  
Vol 14 (14) ◽  
pp. 3508-3515
Author(s):  
Chunhong Cao ◽  
Wei Duan ◽  
Kai Hu ◽  
Fen Xiao
Keyword(s):  
Mr Imaging ◽  
Compressive Sensing ◽  
Tight Frame ◽  
Reference Image

Image Denoising Using a Tight Frame

2006 ◽  
Author(s):  
L. Shen ◽  
M. Papadakis ◽  
I.A. Kakadiaris ◽  
I. Konstantinidis ◽  
D. Kouri ◽  
...  
Keyword(s):  
Image Denoising ◽  
Tight Frame

scholarly journals B-spline tight frame based force matching method

2018 ◽  
Vol 362 ◽  
pp. 208-219 ◽  
Author(s):  
Jianbin Yang ◽  
Guanhua Zhu ◽  
Dudu Tong ◽  
Lanyuan Lu ◽  
Zuowei Shen
Keyword(s):  
Tight Frame ◽  
Matching Method ◽  
B Spline ◽  
Force Matching

Three-channel Symmetric Tight Frame Wavelet Design Method

2013 ◽  
Vol 12 (4) ◽  
pp. 623-631 ◽  
Author(s):  
Ping Zhao ◽  
Chun Zhao
Keyword(s):  
Design Method ◽  
Tight Frame ◽  
Wavelet Design

scholarly journals Characterizations of Tight Frame Wavelets with Special Dilation Matrices

2010 ◽  
Vol 2010 ◽  
pp. 1-26 ◽  
Author(s):  
Huang Yongdong ◽  
Zhu Fengjuan
Keyword(s):  
Linear Space ◽  
Multiresolution Analysis ◽  
Nonnegative Integer ◽  
Tight Frame ◽  
Dilation Matrix ◽  
Low Pass ◽  
Dimension Functions ◽  
Frame Wavelets ◽  
Low Pass Filters

We study all generalized low-pass filters and tight frame wavelets with special dilation matrixM(M-TFW), whereMsatisfiesMd=2Idand generates the checkerboard lattice. Firstly, we study the pseudoscaling function, generalized low-pass filters and multiresolution analysis tight frame wavelets with dilation matrixM(MRA M-TFW), and also give some important characterizations about them. Then, we characterize all M-TFW by showing precisely their corresponding dimension functions which are nonnegative integer valued. Finally, we also show that an M-TFW arises from our MRA construction if and only if the dimension of a particular linear space is either zero or one.

Compressed-Sensing MRI Based on Adaptive Tight Frame in Gradient Domain

2018 ◽  
Vol 49 (5) ◽  
pp. 465-477 ◽  
Author(s):  
Xiaoyu Fan ◽  
Qiusheng Lian ◽  
Baoshun Shi
Keyword(s):  
Compressed Sensing ◽  
Tight Frame ◽  
Gradient Domain

Equiangular tight frame fingerprinting codes

2011 ◽  
Author(s):  
Dustin G. Mixon ◽  
Christopher Quinn ◽  
Negar Kiyavash ◽  
Matthew Fickus
Keyword(s):  
Tight Frame
Sign in / Sign up

Export Citation Format

Share Document