Deep learning for pixel-level image fusion: Recent advances and future prospects

•The difficulties that exist in conventional image fusion research are analyzed.•The advantages of deep learning (DL) techniques for image fusion are discussed.•A detailed review of existing DL-based image fusion methods is presented.•Several generic frameworks for DL-based image fusion are summariz...

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Vydané v:	Information fusion Ročník 42; s. 158 - 173
Hlavní autori:	Liu, Yu, Chen, Xun, Wang, Zengfu, Wang, Z. Jane, Ward, Rabab K., Wang, Xuesong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.07.2018
Predmet:	Convolutional neural network Convolutional sparse representation Deep learning Image fusion Stacked autoencoder Deep learning Stacked autoencoder Convolutional sparse representation Convolutional neural network Image fusion
ISSN:	1566-2535, 1872-6305
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Popis
Shrnutí:	•The difficulties that exist in conventional image fusion research are analyzed.•The advantages of deep learning (DL) techniques for image fusion are discussed.•A detailed review of existing DL-based image fusion methods is presented.•Several generic frameworks for DL-based image fusion are summarized and presented.•Some prospects for the future study of DL-based image fusion are put forward. By integrating the information contained in multiple images of the same scene into one composite image, pixel-level image fusion is recognized as having high significance in a variety of fields including medical imaging, digital photography, remote sensing, video surveillance, etc. In recent years, deep learning (DL) has achieved great success in a number of computer vision and image processing problems. The application of DL techniques in the field of pixel-level image fusion has also emerged as an active topic in the last three years. This survey paper presents a systematic review of the DL-based pixel-level image fusion literature. Specifically, we first summarize the main difficulties that exist in conventional image fusion research and discuss the advantages that DL can offer to address each of these problems. Then, the recent achievements in DL-based image fusion are reviewed in detail. More than a dozen recently proposed image fusion methods based on DL techniques including convolutional neural networks (CNNs), convolutional sparse representation (CSR) and stacked autoencoders (SAEs) are introduced. At last, by summarizing the existing DL-based image fusion methods into several generic frameworks and presenting a potential DL-based framework for developing objective evaluation metrics, we put forward some prospects for the future study on this topic. The key issues and challenges that exist in each framework are discussed.
ISSN:	1566-2535 1872-6305
DOI:	10.1016/j.inffus.2017.10.007