Ghent University Academic Bibliography

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General image fusion for an arbitrary number of inputs using convolutional neural networks

Yifan Xiao (UGent) , Zhixin Guo (UGent) , Peter Veelaert (UGent) and Wilfried Philips (UGent)

(2022) SENSORS. 22(7).
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DMDN : degradation model-based deep network for multi-focus image fusion

Yifan Xiao (UGent) , Zhixin Guo (UGent) , Peter Veelaert (UGent) and Wilfried Philips (UGent)

(2022) SIGNAL PROCESSING-IMAGE COMMUNICATION. 101.
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Deep HDR deghosting by motion-attention fusion network

Yifan Xiao (UGent) , Peter Veelaert (UGent) and Wilfried Philips (UGent)

(2022) SENSORS. 22(20).
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FLOPs-efficient filter pruning via transfer scale for neural network acceleration

Zhixin Guo (UGent) , Yifan Xiao (UGent) , Wenzhi Liao (UGent) , Peter Veelaert (UGent) and Wilfried Philips (UGent)

(2021) JOURNAL OF COMPUTATIONAL SCIENCE. 55.
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Weak segmentation supervised deep neural networks for pedestrian detection

Zhixin Guo (UGent) , Wenzhi Liao (UGent) , Yifan Xiao (UGent) , Peter Veelaert (UGent) and Wilfried Philips (UGent)

(2021) PATTERN RECOGNITION. 119.
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Multi-focus image fusion based on edge-preserving filters

Yifan Xiao (UGent) , Ivana Shopovska (UGent) , Peter Veelaert (UGent) and Wilfried Philips (UGent)

(2019) 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA). In International Conference on Image Processing Theory Tools and Applications
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Deep learning fusion of RGB and depth images for pedestrian detection

Zhixin Guo, Wenzhi Liao (UGent) , Yifan Xiao (UGent) , Peter Veelaert (UGent) and Wilfried Philips (UGent)

(2019) 30th British Machine Vision Conference (BMVC), Proceedings. p.1-13

Academic Bibliography

General image fusion for an arbitrary number of inputs using convolutional neural networks

DMDN : degradation model-based deep network for multi-focus image fusion

Deep HDR deghosting by motion-attention fusion network

FLOPs-efficient filter pruning via transfer scale for neural network acceleration

Weak segmentation supervised deep neural networks for pedestrian detection

Multi-focus image fusion based on edge-preserving filters

Deep learning fusion of RGB and depth images for pedestrian detection

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