NDLI: Multi-focus image fusion with Siamese self-attention network

Content Provider	IET Digital Library
Author	Guo, Xiaopeng Meng, Lingyu Mei, Liye Weng, Yueyun Tong, Hengqing
Abstract	Recently, convolutional neural networks (CNNs) have achieved impressive progress in multi-focus image fusion (MFF). However, it always fails to capture sufficient discrimination features due to the local receptive field limitations of the convolutional operator, restricting most current CNN-based methods’ performance. To address this issue, by leveraging self-attention (SA) mechanism, the authors propose Siamese SA network (SSAN) for MFF. Specifically, two kinds of SA modules, position SA (PSA) and channel SA (CSA) are utilised to model the long-range dependencies across focused and defocused regions in the multi-focus image, alleviating the local receptive field limitations of convolution operators in CNN. To search a better feature representation of the input image for MFF, the captured features obtained by PSA and CSA are further merged through a learnable 1 × 1 convolution operator. The whole pipeline is in a Siamese network fashion to reduce the complexity. After training, the authors SSAN can accomplish well the fusion task with no post-processing. Experiments demonstrate that their approach outperforms other current state-of-the-art methods, not only in subjective visual perception but also in the quantitative assessment.
Starting Page	1339
Ending Page	1346
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 7, May (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0883
Journal	IET Image Processing
Publisher Date	2020-01-17
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Captured Features CNN-based Method Computer Vision And Image Processing Technique Convolution Operators Convolutional Neural Nets Convolutional Neural Network Convolutional Operator Feature Extraction Focused Defocused Region Fusion Task Image Classification Image Enhancement Image Fusion Image Recognition Image Representation Image Resolution Image Sonsor Input Image Knowledge Engineering Technique Learning in AI Local Receptive Field Limitations MFF Multifocus Image Fusion Neural Computing Technique Self-attention Mechanism Siamese SA Network Siamese Self-attention Network Visual Perception
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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