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Junsheng Wu

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2 papers

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EAAI Journal 2024 Journal Article

An efficient frequency domain fusion network of infrared and visible images

Chenwu Wang
Junsheng Wu
Aiqing Fang
Zhixiang Zhu
Pei Wang
Hao Chen

Image fusion plays a crucial role in enhancing the quality and accuracy of semantic segmentation, which is essential for autonomous driving systems. By merging information from multiple imaging sensors or modalities, such as infrared and visible images, image fusion enriches the data and improves the perception capabilities of autonomous vehicles. However, current fusion methodologies often cannot balance model complexity, inference efficiency, and fusion accuracy simultaneously, making them difficult to implement in resource-constrained environments. In response to this, this paper presents a lightweight fusion network based on frequency transformation and deep learning techniques, leveraging wavelet transformation to fuse infrared and visible images. Concisely, the fusion model decomposes input images into different frequency sub-bands using wavelet transforms. It then efficiently fuses the multi-scale feature representations in the frequency domains with a specially designed fusion loss. Compared to traditional fusion approaches, our method not only achieves a better balance between subjective fusion quality and downstream vision tasks but also significantly improves model inference efficiency, paving the way for real-time autonomous driving systems. Extensive experiments on public datasets show that our method can achieve state-of-the-art performance while satisfying parameter efficiency in the context of image fusion and semantic segmentation tasks. Concisely, our approach is nearly 100 × faster while using a model 6000 × smaller in size compared to SegMIF.

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EAAI Journal 2023 Journal Article

Contrastive fusion representation learning for foreground object detection

Pei Wang
Junsheng Wu
Aiqing Fang
Zhixiang Zhu
Chenwu Wang
Pengyuan Mu

This paper investigates the problem of an effective and robust fusion representation method for foreground moving object detection. Many fusion representation learning approaches pay attention to the similarity measurement between the fusion results and source images in texture details and pixel intensity, ignoring the harmful information, e. g. , noise, blur, and extreme illumination. Therefore, the aggregated features of infrared and visible images will introduce much harmful information, affecting the model performance of downstream visual tasks. This paper tackles these problems by proposing a contrastive fusion representation learning method for the foreground moving object detection task, which consists of two major modules: the upstream fusion representation module (FRM) and the downstream foreground moving object detection module (FODM). Unlike the traditional fusion optimization mechanism, the former aims to extract valuable features and reject harmful features via the maximum mutual information theory. The latter is a siamese convolutional neural network to detect foreground moving objects by aggregating the time-sequence images generated by FRM. Experimental results and comparisons with the state-of-the-art on three public datasets (i. e. , TNO, MF, and cross-modal FOD dataset of infrared and visible images), validate the effectiveness, robustness, and overall superiority of the proposed contrast fusion representation learning method. Concisely, our contrastive fusion representation learning has gained 53. 9 %, 43. 2 %, 46. 4 %, 52. 3 %, 2. 2 %, 87. 1 %, 3. 5 % on EI, SF, DF, AG, MI, and Nabf metrics compared with the best competitors.

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