Fusion of multispectral and panchromatic images via convolutional sparse representation and morphological filter
Authors: 
Jiao Jiao, Depeng Chen, Shaobo Yu, Xin GuoAuthors Info & Claims
Jiao Jiao, Depeng Chen, Shaobo Yu, Xin GuoAuthors Info & ClaimsPages 543 - 548
Abstract
Aimed at the lack of the spectral information preservation and the spatial detail injection in fusion of multispectral (MS) and panchromatic (PAN) images, the paper proposed a pansharpening algorithm based on convolutional sparse representation (CSR) and morphological filter (MF) by introducing a recently emerged signal decomposition model known as CSR. Firstly, the PAN and MS images are decomposed to obtain a base layer and a detail layer, respectively. Secondly, the fusion rule of the base layers which based on MF and high-pass modulation (HPM) scheme is proposed to retain more details. For the fusion of detail layers, maximum selection scheme based on activity maps and CSR model are adopted for fusion. Finally, the fusion results of the base layer and detail layer are reconstructed to obtain the final fusion image. The experimental results show that the proposed method is superior to the traditional methods and some current popular fusion methods from the visual effects and the objective indices.
References
[1]
BIKASH M, SANJAY A, RUTUPARNA P.: A survey on region based image fusion methods[J]. Information Fusion 48(8), 119–132 (2019).
[2]
JIAO J, WU L D.: Pansharpening with a gradient domain GIF based on NSST[J]. Electronics 8(2), 229: 1–28 (2019).
[3]
DONG W, FU F, SHI G, : Hyperspectral image super-resolution via non-negative structured sparse representation[J]. IEEE Transactions on Image Processing 25(5), 2337–2352 (2016).
[4]
AMRO I, MATEOS J, VEGA M, : A survey of classical methods and new trends in pansharpening of multispectral images[J]. EURASIP Journal on Advances in Signal Processing 1(79), 1–22 (2011).
[5]
GHASSEMIAN H.: A review of remote sensing image fusion methods[J]. Information Fusion 32(PA), 75–89 (2016).
[6]
VIVONE G, ALPARONE L, CHANUSSOT J, : A critical comparison among pansharpening algorithms [J]. IEEE Transactions on Geoscience & Remote Sensing 53(5), 2565–2586 (2015).
[7]
THOMAS C, RANCHIN T, WALD L, : Synthesis of multispectral images to high spatial resolution, A critical review of fusion methods based on remote sensing physics[J]. IEEE Transactions on Geoscience and Remote Sensing 46(5), 1301–1312 (2008).
[8]
GUO L Q, CAO X, LIU L.: Dual-tree biquaternion wavelet transform and its application to color image fusion[J]. Signal Processing 171(6), 107513 (2020).
[9]
ANANDHI D, VALLI S.: An algorithm for multi-sensor image fusion using maximum a posteriori and nonsubsampled contourlet transform[J]. Computers & Electrical Engineering 65, 139–152 (2018).
[10]
JIAO J, WU L D.: Image restoration for the MRA-based pansharpening method[J]. IEEE Access 8, 13694–13709 (2020).
[11]
FALLAH M, AZADBAKHT M.: Fusion of thermal infrared and visible images based on multi-scale transform and sparse representation[J]. Journal of Geospatial Information Technology 8(3), 39–59 (2021).
[12]
BRUCKSTEIN A M, DONOHO D L, ELAD M.: From sparse solutions of systems of equations to sparse modeling of signals and images[J]. SIAM Review, 2009, 51(1), 34–81.
[13]
DONOHO D L.: Compressed sensing[J]. IEEE Transactions on Information Theory 52(4), 1289–1306 (2006).
[14]
GHAHREMANI M, GHASSEMIAN H.: A compressed-sensing-based pan-sharpening method for spectral distortion reduction[J]. IEEE Transactions on Geoscience and Remote Sensing 54(4), 2194–2206 (2016).
[15]
YANG S, ZHANG K, WANG M.: Learning low-rank decomposition for pan-sharpening with spatial-spectral offsets[J]. IEEE Transactions on Neural Networks & Learning Sys-tems 29(8), 3647–3657 (2018).
[16]
GHAHREMANI M, GHASSEMIAN H.: Remote sensing image fusion using ripplet trans-form and compressed sensing[J]. IEEE Geoscience and Remote Sensing Letters 12(3), 502–506 (2015).
[17]
SAXENA N, BALASUBRAMANIAN R.: A pansharpening scheme using spectral graph wavelet transforms and convolutional neural networks[J]. International Journal of Re-mote Sensing 42(8),2898–2919 (2021).
[18]
YANG B, LI S.: Multifocus image fusion and restoration with sparse representation[J]. IEEE Transactions on Instrumentation and Measurement 59(4), 884–892 (2010).
[19]
MOONON A U, HU J, Li S.: Remote sensing image fusion method based on nonsubsampled shearlet transform and sparse representation[J]. Sensing and Imaging 16(1), 23 (2015).
[20]
LIU Y, LIU S P, WANG Z F.: A general framework for image fusion based on multi-scale transform and sparse representation[J]. Information Fusion 24, 147–164 (2015).
[21]
XING C D, WANG M L, CHONG D, : Using taylor expansion and convolutional sparse representation for image fusion[J]. Neurocomputing 402, 437–455 (2020).
[22]
WANG H Y, QIU H L, LI W S.: Nonconvex dictionary learning based visual tracking method[J]. Signal Processing 172, 107535 (2020).
[23]
LIU Y, CHEN X, WARD R K, : Image fusion with convolutional sparse representation[J]. IEEE Signal Processing Letters 23(12), 1882–1886 (2016).
[24]
GARZELLI A, NENCINI F, CAPOBIANCO L.: Optimal MMSE pan sharpening of very high resolution multispectral image[J]. IEEE Transactions on Geoscience and Remote Sensing 46(1), 228–236 (2008).
[25]
RESTAINO R, VIVONE G, DALLA M M, : Fusion of multispectral and panchromatic images based on morphological operators[J]. IEEE Transactions on Image Processing 25(6), 2882–2895 (2016).
[26]
RESTAINO R, MURA M D, VIVONE G, : Context-adaptive pansharpening based on image segmentation[J]. IEEE Transactions on Geoscience and Remote Sensing 55(2), 753–766 (2017).
[27]
LIU Y, CHEN X, WARD R K, : Medical image fusion via convolutional sparsity based morphological component analysis[J]. IEEE Signal Processing Letters 26(3), 485–489 (2019).
[28]
YANG B, LI S.: Pixel-level image fusion with simultaneous orthogonal matching pursuit[J]. Information Fusion 13(1), 10–19 (2012).
[29]
WOHLBERG B.: Efficient algorithms for convolutional sparse representation[J]. IEEE Transactions on Image Processing 25(1), 301–315 (2016).
[30]
JIAO J, WU L D.: Fusion of panchromatic and multispectral images via morphological operator and improved PCNN in mixed multiscale domain [C], Proceeding of 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing, 1-11 (2018)
Index Terms
- Fusion of multispectral and panchromatic images via convolutional sparse representation and morphological filter
Index terms have been assigned to the content through auto-classification.
Recommendations
Multi-focus noisy image fusion based on gradient regularized convolutional sparse representatione
MMAsia '20: Proceedings of the 2nd ACM International Conference on Multimedia in AsiaThe method proposes a multi-focus noisy image fusion algorithm combining gradient regularized convolutional sparse representatione and spatial frequency. Firstly, the source image is decomposed into a base layer and a detail layer through two-scale ...
A new multi-focus image fusion quality assessment method with convolutional sparse representation
AbstractAssessing image fusion quality purposefully is a challenging task due to the diversities of fused features. In this work, a specific multi-focus image fusion quality assessment method is proposed based on joint image layering and convolutional ...
Novel and fast EMD-based image fusion via morphological filter
AbstractThis paper presents a novel and fast EMD-based (empirical mode decomposition-based) image fusion approach via morphological filter. Firstly, we develop a multi-channel bidimensional EMD method based on morphological filter to conduct image fusion. ...
Comments
Information & Contributors
Information
Published In
October 2022
753 pages
ISBN:9781450397780
DOI:10.1145/3569966
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 December 2022
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
CSSE 2022
CSSE 2022: 2022 5th International Conference on Computer Science and Software Engineering
October 21 - 23, 2022
Guilin, China
Acceptance Rates
Overall Acceptance Rate 33 of 74 submissions, 45%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 20Total Downloads
- Downloads (Last 12 months)5
- Downloads (Last 6 weeks)2
Reflects downloads up to 25 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format