LRNet: lightweight attention-oriented residual fusion network for light field salient object detection

Light field imaging contains abundant scene structure information, which can improve the accuracy of salient object detection in challenging tasks and has received widespread attention. However, how to apply the abundant information of light field imaging to salient object detection still faces enor...

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Bibliographic Details
Published in:Scientific reports 2024-10, Vol.14 (1), p.26030-12, Article 26030
Main Authors: Ma, Shuai, Zhu, Xusheng, Xu, Long, Zhou, Li, Chen, Daixin
Format: Article
Language:English
Subjects:
639/166/987
639/705/117
Accuracy
Cameras
Deep learning
Electrons
Humanities and Social Sciences
Information processing
Light
Light field
Lightweight attention
Methods
multidisciplinary
Residual ConvLSTM
Saliency detection
Science
Science (multidisciplinary)
Semantics
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Summary:Light field imaging contains abundant scene structure information, which can improve the accuracy of salient object detection in challenging tasks and has received widespread attention. However, how to apply the abundant information of light field imaging to salient object detection still faces enormous challenges. In this paper, the lightweight attention and residual convLSTM network is proposed to address this issue, which is mainly composed of the lightweight attention-based feature enhancement module (LFM) and residual convLSTM-based feature integration module (RFM). The LFM can provide an attention map for each focal slice through the attention mechanism to focus on the features related to the object, thereby enhancing saliency features. The RFM leverages the residual mechanism and convLSTM to fully utilize the spatial structural information of focal slices, thereby achieving high-precision feature fusion. Experimental results on three publicly available light field datasets show that the proposed method surpasses the existing 17 state-of-the-art methods and achieves the highest score among five quantitative indicators.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-76874-0