X-ray image separation via coupled dictionary learning

Nikos Deligiannis; João F. C. Mota; Bruno Cornelis; Miguel Raul Dias Rodrigues; Ingrid Daubechies

doi:10.1109/ICIP.2016.7533017

X-ray image separation via coupled dictionary learning

Nikos Deligiannis, João F. C. Mota, Bruno Cornelis, Miguel Raul Dias Rodrigues, Ingrid Daubechies

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Citations (Scopus)

Abstract

In support of art investigation, we propose a new source separation method that unmixes a single X-ray scan acquired from double-sided paintings. Unlike prior source separation methods, which are based on statistical or structural incoherence of the sources, we use visual images taken from the front- and back-side of the panel to drive the separation process. The coupling of the two imaging modalities is achieved via a new multi-scale dictionary learning method. Experimental results demonstrate that our method succeeds in the discrimination of the sources, while state-of-the-art methods fail to do so.

Original language	English
Title of host publication	2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
Publisher	IEEE
Pages	3533-3537
Number of pages	5
ISBN (Electronic)	9781467399616
DOIs	https://doi.org/10.1109/ICIP.2016.7533017
Publication status	Published - Aug 2016
Event	23rd IEEE International Conference on Image Processing - Phoenix Convention Center, Phoenix, United States Duration: 25 Sept 2016 → 28 Sept 2016

Conference

Conference	23rd IEEE International Conference on Image Processing
Abbreviated title	ICIP 2016
Country/Territory	United States
City	Phoenix
Period	25/09/16 → 28/09/16

Keywords

Dictionary learning
Image decomposition
Image separation
Multi-modal imaging
Side information

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

Access to Document

10.1109/ICIP.2016.7533017

Cite this

@inproceedings{9a6d276bcf1241b38c1335b4c32d4269,

title = "X-ray image separation via coupled dictionary learning",

abstract = "In support of art investigation, we propose a new source separation method that unmixes a single X-ray scan acquired from double-sided paintings. Unlike prior source separation methods, which are based on statistical or structural incoherence of the sources, we use visual images taken from the front- and back-side of the panel to drive the separation process. The coupling of the two imaging modalities is achieved via a new multi-scale dictionary learning method. Experimental results demonstrate that our method succeeds in the discrimination of the sources, while state-of-the-art methods fail to do so.",

keywords = "Dictionary learning, Image decomposition, Image separation, Multi-modal imaging, Side information",

author = "Nikos Deligiannis and Mota, {Jo{\~a}o F. C.} and Bruno Cornelis and Rodrigues, {Miguel Raul Dias} and Ingrid Daubechies",

year = "2016",

month = aug,

doi = "10.1109/ICIP.2016.7533017",

language = "English",

pages = "3533--3537",

booktitle = "2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings",

publisher = "IEEE",

address = "United States",

note = "23rd IEEE International Conference on Image Processing, ICIP 2016 ; Conference date: 25-09-2016 Through 28-09-2016",

}

Deligiannis, N, Mota, JFC, Cornelis, B, Rodrigues, MRD & Daubechies, I 2016, X-ray image separation via coupled dictionary learning. in 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings., 7533017, IEEE, pp. 3533-3537, 23rd IEEE International Conference on Image Processing, Phoenix, United States, 25/09/16. https://doi.org/10.1109/ICIP.2016.7533017

TY - GEN

T1 - X-ray image separation via coupled dictionary learning

AU - Deligiannis, Nikos

AU - Mota, João F. C.

AU - Cornelis, Bruno

AU - Rodrigues, Miguel Raul Dias

AU - Daubechies, Ingrid

PY - 2016/8

Y1 - 2016/8

N2 - In support of art investigation, we propose a new source separation method that unmixes a single X-ray scan acquired from double-sided paintings. Unlike prior source separation methods, which are based on statistical or structural incoherence of the sources, we use visual images taken from the front- and back-side of the panel to drive the separation process. The coupling of the two imaging modalities is achieved via a new multi-scale dictionary learning method. Experimental results demonstrate that our method succeeds in the discrimination of the sources, while state-of-the-art methods fail to do so.

AB - In support of art investigation, we propose a new source separation method that unmixes a single X-ray scan acquired from double-sided paintings. Unlike prior source separation methods, which are based on statistical or structural incoherence of the sources, we use visual images taken from the front- and back-side of the panel to drive the separation process. The coupling of the two imaging modalities is achieved via a new multi-scale dictionary learning method. Experimental results demonstrate that our method succeeds in the discrimination of the sources, while state-of-the-art methods fail to do so.

KW - Dictionary learning

KW - Image decomposition

KW - Image separation

KW - Multi-modal imaging

KW - Side information

UR - http://www.scopus.com/inward/record.url?scp=85006797203&partnerID=8YFLogxK

U2 - 10.1109/ICIP.2016.7533017

DO - 10.1109/ICIP.2016.7533017

M3 - Conference contribution

AN - SCOPUS:85006797203

SP - 3533

EP - 3537

BT - 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings

PB - IEEE

T2 - 23rd IEEE International Conference on Image Processing

Y2 - 25 September 2016 through 28 September 2016

ER -

X-ray image separation via coupled dictionary learning

Abstract

Conference

Keywords

ASJC Scopus subject areas

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