Cargando…

Dual-Tree Complex Wavelet Transform and Image Block Residual-Based Multi-Focus Image Fusion in Visual Sensor Networks

This paper presents a novel framework for the fusion of multi-focus images explicitly designed for visual sensor network (VSN) environments. Multi-scale based fusion methods can often obtain fused images with good visual effect. However, because of the defects of the fusion rules, it is almost impos...

Descripción completa

Detalles Bibliográficos
Autores principales: Yang, Yong, Tong, Song, Huang, Shuying, Lin, Pan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4299020/
https://www.ncbi.nlm.nih.gov/pubmed/25587878
http://dx.doi.org/10.3390/s141222408
_version_ 1782353339120353280
author Yang, Yong
Tong, Song
Huang, Shuying
Lin, Pan
author_facet Yang, Yong
Tong, Song
Huang, Shuying
Lin, Pan
author_sort Yang, Yong
collection PubMed
description This paper presents a novel framework for the fusion of multi-focus images explicitly designed for visual sensor network (VSN) environments. Multi-scale based fusion methods can often obtain fused images with good visual effect. However, because of the defects of the fusion rules, it is almost impossible to completely avoid the loss of useful information in the thus obtained fused images. The proposed fusion scheme can be divided into two processes: initial fusion and final fusion. The initial fusion is based on a dual-tree complex wavelet transform (DTCWT). The Sum-Modified-Laplacian (SML)-based visual contrast and SML are employed to fuse the low- and high-frequency coefficients, respectively, and an initial composited image is obtained. In the final fusion process, the image block residuals technique and consistency verification are used to detect the focusing areas and then a decision map is obtained. The map is used to guide how to achieve the final fused image. The performance of the proposed method was extensively tested on a number of multi-focus images, including no-referenced images, referenced images, and images with different noise levels. The experimental results clearly indicate that the proposed method outperformed various state-of-the-art fusion methods, in terms of both subjective and objective evaluations, and is more suitable for VSNs.
format Online
Article
Text
id pubmed-4299020
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-42990202015-01-26 Dual-Tree Complex Wavelet Transform and Image Block Residual-Based Multi-Focus Image Fusion in Visual Sensor Networks Yang, Yong Tong, Song Huang, Shuying Lin, Pan Sensors (Basel) Article This paper presents a novel framework for the fusion of multi-focus images explicitly designed for visual sensor network (VSN) environments. Multi-scale based fusion methods can often obtain fused images with good visual effect. However, because of the defects of the fusion rules, it is almost impossible to completely avoid the loss of useful information in the thus obtained fused images. The proposed fusion scheme can be divided into two processes: initial fusion and final fusion. The initial fusion is based on a dual-tree complex wavelet transform (DTCWT). The Sum-Modified-Laplacian (SML)-based visual contrast and SML are employed to fuse the low- and high-frequency coefficients, respectively, and an initial composited image is obtained. In the final fusion process, the image block residuals technique and consistency verification are used to detect the focusing areas and then a decision map is obtained. The map is used to guide how to achieve the final fused image. The performance of the proposed method was extensively tested on a number of multi-focus images, including no-referenced images, referenced images, and images with different noise levels. The experimental results clearly indicate that the proposed method outperformed various state-of-the-art fusion methods, in terms of both subjective and objective evaluations, and is more suitable for VSNs. MDPI 2014-11-26 /pmc/articles/PMC4299020/ /pubmed/25587878 http://dx.doi.org/10.3390/s141222408 Text en © 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yang, Yong
Tong, Song
Huang, Shuying
Lin, Pan
Dual-Tree Complex Wavelet Transform and Image Block Residual-Based Multi-Focus Image Fusion in Visual Sensor Networks
title Dual-Tree Complex Wavelet Transform and Image Block Residual-Based Multi-Focus Image Fusion in Visual Sensor Networks
title_full Dual-Tree Complex Wavelet Transform and Image Block Residual-Based Multi-Focus Image Fusion in Visual Sensor Networks
title_fullStr Dual-Tree Complex Wavelet Transform and Image Block Residual-Based Multi-Focus Image Fusion in Visual Sensor Networks
title_full_unstemmed Dual-Tree Complex Wavelet Transform and Image Block Residual-Based Multi-Focus Image Fusion in Visual Sensor Networks
title_short Dual-Tree Complex Wavelet Transform and Image Block Residual-Based Multi-Focus Image Fusion in Visual Sensor Networks
title_sort dual-tree complex wavelet transform and image block residual-based multi-focus image fusion in visual sensor networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4299020/
https://www.ncbi.nlm.nih.gov/pubmed/25587878
http://dx.doi.org/10.3390/s141222408
work_keys_str_mv AT yangyong dualtreecomplexwavelettransformandimageblockresidualbasedmultifocusimagefusioninvisualsensornetworks
AT tongsong dualtreecomplexwavelettransformandimageblockresidualbasedmultifocusimagefusioninvisualsensornetworks
AT huangshuying dualtreecomplexwavelettransformandimageblockresidualbasedmultifocusimagefusioninvisualsensornetworks
AT linpan dualtreecomplexwavelettransformandimageblockresidualbasedmultifocusimagefusioninvisualsensornetworks