Prof. Liefu Ai Profile

Prof. Liefu Ai

at Anqing Normal Univ

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Publications (2)

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SPIE Journal Paper | 22 December 2023

EDGE-Net: an edge-guided enhanced network for RGB-T salient object detection

Xin Zheng, Boyang Wang, Liefu Ai, Pan Tang, Deyang Liu

JEI, Vol. 32, Issue 06, 063032, (December 2023) https://doi.org/10.1117/12.10.1117/1.JEI.32.6.063032

KEYWORDS: RGB color model, Feature extraction, Image fusion, Convolution, Object detection, Cameras, Thermography, Image segmentation, Feature fusion, Education and training

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With similar textures, dark backgrounds, and complex scenes, RGB images are usually unable to provide discriminative information for model training, which often leads to inaccurate prediction results. Compared with RGB salient object detection (SOD) methods, RGB-T SOD have thermal infrared (TIR) information as an informational supplement. As a result, RGB-T SOD can adapt to more complex environments and achieve better results. However, existing methods do not efficiently integrate features between different modalities and do not fully exploit spatial information from the shallow-level features. Accordingly, we propose an EDGE-Net. First, we propose an edge extraction module to capture the edge information in the shallow-level features and use it to guide the subsequent decoding. The original edge features are then weighted after channel attention processing. Second, to additionally suppress the noise of the shallow-level features, we design a global information extraction module. In this module, multiple convolutions are used instead of single convolutions to reduce the computational effort, and convolutions with different dilation rates are used to obtain different receptive fields. We conduct extensive experiments on the RGB-T dataset and show that the proposed method achieves superior performance compared to several state-of-the-art algorithms. The code and results of our method are available in a Github repository available at: https://github.com/BorreloadD/EDGE-Net.git.

Proceedings Article | 10 October 2020 Poster + Presentation + Paper

Dual-level light field reconstruction network based on directional EPI volume

Deyang Liu, Wenfa Zhan, Guangjun Wang, Xin Zheng, Liefu Ai

Proceedings Volume 11550, 115500X (2020) https://doi.org/10.1117/12.2573411

KEYWORDS: Spatial resolution, Data modeling, 3D image processing, 3D image reconstruction, Image processing

Read Abstract +

Light field (LF) imaging, which can capture spatial and angular information of light-rays in one shot, has received increasing attention. However, the well-known LF spatio-angular trade-off problem has restricted many applications of LF imaging. In order to alleviate this problem, this paper put forward a dual-level LF reconstruction network to improve LF angular resolution with sparselysampled LF inputs. Instead of using 2D or 3D LF representation in reconstruction process, this paper propose an LF directional EPI volume representation to synthesize the full LF. The proposed LF representation can encourage an interaction of spatial-angular dimensions in convolutional operation, which is benefit for recovering the lost texture details in synthesized sub-aperture images (SAIs). In order to extract the high-dimensional geometric features of the angular mapping from low angular resolution inputs to high angular full LF, a dual-level deep network is introduced. The proposed deep network consists of an SAI synthesis sub-network and a detail refinement sub-network, which allows LF reconstruction in a dual-level constraint (i.e., from coarse to fine). Our network model is evaluated on several real-world LF scenes datasets, and extensive experiments validate that the proposed model outperforms the state-of-the-arts and achieves a better reconstruct SAIs perceptual quality as well.

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