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Wilfried Philips

6 accepted papers

2024

CARB-Net: Camera-Assisted Radar-Based Network for Vulnerable Road User Detection

Wei-Yu Lee*, Martin Dimitrievski, David Van Hamme, Jan Aelterman, Ljubomir Jovanov, Wilfried Philips
ECCV 2024poster

"Ensuring a reliable perception of vulnerable road users is crucial for safe autonomous driving. Radar stands out as an appealing sensor choice due to its resilience in adverse weather, cost-effectiveness, depth sensing capabilities, and established role in adaptive cruise control. Nevertheless, rad…

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2024

Unmixing Diffusion for Self-Supervised Hyperspectral Image Denoising

Haijin Zeng, Jiezhang Cao, Kai Zhang, Yongyong Chen, Hiep Luong, Wilfried Philips
CVPR 2024poster

Hyperspectral images (HSIs) have extensive applications in various fields such as medicine agriculture and industry. Nevertheless acquiring high signal-to-noise ratio HSI poses a challenge due to narrow-band spectral filtering. Consequently the importance of HSI denoising is substantial especially f…

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2024

Wavelength-Embedding-guided Filter-Array Transformer for Spectral Demosaicing

Haijin Zeng*, Hiep Luong, Wilfried Philips
ECCV 2024poster

"Spectral imaging offers the capability to unveil hidden details within the world around us. However, to fully harness this potential, it is imperative to develop effective spectral demosaicing techniques. Despite the success of learning based spectral demosaicing methods, three challenges hinder th…

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2022

Gradient Variance Loss for Structure-Enhanced Image Super-Resolution

Lusine Abrahamyan, Anh Minh Truong, Wilfried Philips, Nikos Deligiannis
ICASSP 2022accepted

Recent success in the field of single image super-resolution (SISR) is achieved by optimizing deep convolutional neural networks (CNNs) in the image space with the L1 or L2 loss. However, when trained with these loss functions, models usually fail to recover sharp edges present in the high-resolutio…

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2019

Matrix Completion with Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference

Tien Huu Do, Duc Minh Nguyen, Evaggelia Tsiligianni, Angel Lopez Aguirre, Valerio Panzica La Manna, Frank J. Pasveer +2
ICASSP 2019accepted

Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional ty…

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