Mr. Rui Zhao Profile

Rui Zhao

at The Hong Kong Polytechnic University

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Proceedings Article | 13 March 2021 Paper

Attention-based cross-modality interaction for multispectral pedestrian detection

Tianshan Liu, Rui Zhao, Kin-Man Lam

Proceedings Volume 11766, 1176603 (2021) https://doi.org/10.1117/12.2590661

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Multispectral pedestrian detection has attracted extensive attention, as paired RGB-thermal images can provide complementary patterns to deal with illumination changes in realistic scenarios. However, most of the existing deep-learning-based multispectral detectors extract features from RGB and thermal inputs separately, and fuse them by a simple concatenation operation. This fusion strategy is suboptimal, as undifferentiated concatenation for each region and feature channel may hamper the optimal selection of complementary features from different modalities. To address this limitation, in this paper, we propose an attention-based cross-modality interaction (ACI) module, which aims to adaptively highlight and aggregate the discriminative regions and channels of the feature maps from RGB and thermal images. The proposed ACI module is deployed into multiple layers of a two-branch-based deep architecture, to capture the cross-modal interactions from diverse semantic levels, for illumination-invariant pedestrian detection. Experimental results on the public KAIST multispectral pedestrian benchmark show that the proposed method achieves state-of-the-art detection performance.

Proceedings Article | 1 June 2020 Paper

Elastic net with adaptive weight for image denoising

Jun Xiao, Rui Zhao, Kin-Man Lam

Proceedings Volume 11515, 1151506 (2020) https://doi.org/10.1117/12.2566961

KEYWORDS: Image denoising, Associative arrays, Performance modeling, Denoising, RGB color model, Image restoration, Data modeling, Chemical elements, Visual process modeling, Feature extraction

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Sparse models have been widely used in image denoising, and have achieved state-of-the-art performance in past years. Dictionary learning and sparse code estimation are the two key issues for sparse models. When a dictionary is learned, sparse code estimation is equivalent to a general least absolute shrinkage and selection operator (LASSO) problem. However, there are two limitations of LASSO: 1). LASSO gives rise to a biased estimation. 2). LASSO cannot select highly correlated features simultaneously. In recent years, methods for dictionary construction based on the nonlocal self-similarity property and weighted sparse model, relying on noise estimation, have been proposed. These methods can reduce the biased gap of the estimation, and thus achieve promising results for image denoising. In this paper, we propose an elastic net with adaptive weight for image denoising. Our proposed model can achieve nearly unbiased estimation and select highly correlated features. Experimental results show that our proposed method outperforms other state-of-the-art image denoising methods.

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