http://iet.metastore.ingenta.com
1887

Tri-decomposition model for image recovery

Tri-decomposition model for image recovery

For access to this article, please select a purchase option:

Buy eFirst article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
Electronics Letters — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This Letter reveals the feasibility of decomposing a matrix into three component matrices for image recovery. Image recovery emerges in many areas, such as image processing, computer vision, and pattern recognition. Recently, the low rank assumption-based image recovery methods catch the researcher's attention. The authors assume the real data matrix has low rank and the error matrix is sparse. However, they are limited to the low-rank component being exactly low-rank, and the sparse component being exactly sparse. Either or both these assumptions are not exactly satisfied in practice and should be relaxed. This Letter presents a tri-decomposition method for dealing with the image data corrupted by both large sparse noise and small dense noise. The method parts the observed data into the clean data, sparse noise, and dense noise by different measure functions. Extensive experiments on face images and surveillance videos demonstrate the effectiveness of the proposed method.

http://iet.metastore.ingenta.com/content/journals/10.1049/el.2018.5718
Loading

Related content

content/journals/10.1049/el.2018.5718
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address