设为首页
收藏本站
网站地图
|
English
|
公务邮箱
About the library
Background
History
Leadership
Organization
Readers' Guide
Opening Hours
Collections
Help Via Email
Publications
Electronic Information Resources
The
proximal alternating iterative hard thresholding method for minimization, with complexity
详细信息
查看全文
作者:
Fan Yang
a
;
kiraofsin@yahoo.co.
jp
"
class
="auth_
mail
"
title
="E-
mail
the
corresponding
author
;
Yi Shen
a
;
yshen@zstu.edu.cn"
class
="auth_
mail
"
title
="E-
mail
the
corresponding
author
;
Zhi Song Liu
b
;
liuzhisong@zjou.edu.cn"
class
="auth_
mail
"
title
="E-
mail
the
corresponding
author
关键词:
Sparse approximation
;
Alternating minimization
;
Hard thresholding
;
Tight wavelet frame
;
Kurdyka&ndash
;
Łojasiewicz property
刊名:Journal of Computational and Applied Ma
the
matics
出版年:2017
出版时间:February 2017
年:2017
卷:311
期:Complete
页码:115-129
全文大小:964 K
文摘
Since digital images are usually sparse in
the
wavelet frame domain, some nonconvex minimization models based on wavelet frame have been proposed and sparse approximations have been widely used in image restoration in recent years. Among
the
m,
the
proximal alternating iterative hard thresholding method is proposed in this paper to solve
the
nonconvex model based on wavelet frame. Through combining
the
proposed algorithm with
the
iterative hard thresholding algorithm which is well studied in compressed sensing
the
ory, this paper proves that
the
complexity of
the
proposed method is
class="mathmlsrc">
title="View
the
MathML source"
class
="mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0377042716303326&_mathId=si7.gif&_user=111111111&_pii=S0377042716303326&_rdoc=1&_issn=03770427&md5=f3d7fa815146e13f25f3b957a6de3aec">
class="imgLazyJSB inlineImage" height="18" width="63" alt="View
the
MathML source"
title
="View
the
MathML source" src="/sd/grey_pxl.gif" data-inlimgeid="1-s2.0-S0377042716303326-si7.gif">
the MathML source"
title
="View
the
MathML source" src="http://origin-ars.els-cdn.com/content/image/1-s2.0-S0377042716303326-si7.gif">
class="mathContainer hidden">
class="mathCode">
O
(
1
/
k
)
. On
the
o
the
r hand, a more general nonconvex–nonsmooth model is adopted and
the
pseudo proximal alternating linearized minimization method is developed to solve
the
above problem. With
the
Kurdyka–Łojasiewicz (KL) property, it is proved that
the
sequence generated by
the
proposed algorithm converges to some critical points of
the
corresponding
model. Finally,
the
proposed method is applied to restore
the
blurred noisy gray images. As
the
numerical results reveal,
the
performance of
the
proposed method is comparable or better than some well-known convex image restoration methods.
NGLC 2004-2010.National Geological Library of China All Rights Reserved.
Add:29 Xueyuan Rd,Haidian District,Beijing,PRC. Mail Add: 8324 mailbox 100083
For exchange or info please contact us via
email
.