Recent advances via convolutional sparse representation model for pixel-level image fusion

Image fusion aims to integrate complementary information from different source images into the final output image. This plays a significant role in high-level vision tasks. However, image fusion methods based on sparse representation (SR) or conventional multiscale transform (MST) have some drawback...

Full description

Saved in:
Bibliographic Details
Published in:Multimedia tools and applications Vol. 83; no. 17; pp. 52899 - 52930
Main Authors: Pan, Yue, Lan, Tianye, Xu, Chongyang, Zhang, Chengfang, Feng, Ziliang
Format: Journal Article
Language:English
Published: New York Springer US 01.05.2024
Springer Nature B.V
Subjects:
Algorithms
Computer Communication Networks
Computer Science
Computer vision
Data Structures and Information Theory
Decomposition
Deep learning
Dictionaries
Infrared imagery
Medical imaging
Medical research
Multimedia
Multimedia Information Systems
Performance evaluation
Pixels
Representations
Special Purpose and Application-Based Systems
Track 6: Computer Vision for Multimedia Applications
Trends
Wavelet transforms
Online convolutional sparse coding
Convolutional sparse representation
Online convolutional dictionary learning
Convolutional dictionary learning
Image fusion
ISSN:1573-7721, 1380-7501, 1573-7721
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Image fusion aims to integrate complementary information from different source images into the final output image. This plays a significant role in high-level vision tasks. However, image fusion methods based on sparse representation (SR) or conventional multiscale transform (MST) have some drawbacks that are difficult to overcome. As an alternative form of SR, convolutional sparse representation (CSR) has the advantages of detail preservation and shift-invariance, which can overcome the shortcomings of SR- and MST-based fusion methods. Since CSR has been widely used in the field of image fusion and has advanced this field to a great extent, it is necessary to conduct a comprehensive investigation of image fusion based on CSR. To the best of our knowledge, there are no previous papers reviewing and evaluating CSR-based fusion methods, and this study is the first retrospective. In this paper, we focus on CSR-based image fusion methods and review the recent advances in pixel-level image fusion based on CSR. In the experimental part of the paper, multifocal images, infrared-visible images, and multimodal medical images are used as test images to compare and evaluate the performance of different image fusion methods. In addition, the future trend of CSR-based image fusion is discussed. This paper is expected to serve as a resource of reference for both researchers and general learners seeking an overview of CSR-based image fusion.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-023-17584-z