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XingHua Wang

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3 papers

1 author row

EAAI Journal 2025 Journal Article

Dual-path aggregation transformer network for super-resolution with images occlusions and variability

Qinghui Chen
LunQian Wang
Zekai Zhang
XingHua Wang
Weilin Liu
Bo Xia
Hao Ding
Jinglin Zhang

While Transformer-based approaches have recently achieved notable success in super-resolution, their extensive computational requirements impede widespread practical adoption. High-resolution meteorological satellite cloud imagery is essential for weather analysis and forecasting. Enhancing image resolution through super-resolution techniques facilitates the accurate identification and localization of geographic features by meteorological systems. However, current super-resolution methods fail to restore the intricacies of cloud formations and complex regions fully. This research introduces a novel dual-path aggregation Transformer network (DPAT) tailored to enhance the super-resolution of meteorological satellite cloud images. The DPAT network adeptly captures cloud imagery's subtle details and textures, effectively addressing occlusions and the variability inherent in satellite imagery. It bolsters the model's ability to manage the complex attributes of cloud images through the introduction of the Dual-path Aggregation Self-Attention (DASA) mechanism and the Multi-scale Feature Aggregation Block (MFAB), thereby enhancing performance in processing intricate cloud features. The DASA mechanism synthesizes features across spatial, depth, and channel dimensions via a dual-path approach, thoroughly exploiting feature correlations. The MFAB, designed to supplant the multilayer perceptron, incorporates shift convolution and a multi-scale interaction block to augment feature information, compensating for the deficiency in local information absorption due to fixed receptive fields. Experimental outcomes indicate that DPAT delivers superior super-resolution outcomes. With a parameter count of only 32% of the Enhanced Deep Residual Network (EDSR) or 77% of the Image Restoration using Shift Window Transformer (SwinIR), DPAT matches SwinIR's performance on the satellite cloud dataset. Moreover, DPAT balances accuracy and parameter economy across various datasets. This technology is expected to improve image super-resolution capabilities in multiple fields such as human action recognition and industrial recognition, and indirectly improve the accuracy of image perception tasks.

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EAAI Journal 2024 Journal Article

A multi-modal driver emotion dataset and study: Including facial expressions and synchronized physiological signals

Guoliang Xiang
Song Yao
Hanwen Deng
Xianhui Wu
XingHua Wang
Qian Xu
Tianjian Yu
Kui Wang

To address the limitations of databases in the field of emotion recognition and to cater to the trend of integrating data from multiple sources, we have established a multi-modal emotional dataset based on spontaneous expression of drivers. By selecting emotional induction materials and inducing emotions before each driving task, facial expression videos and synchronous physiological signals of the drivers during driving were collected. The dataset includes records of 64 participants under five different emotions (neutral, happy, angry, sad, and fear), and the emotional valence, arousal, and peak time of all participants in each driving task were recorded. To analyze the dataset, spatio-temporal convolutional neural networks were designed to analyze the different modalities of data with varying durations in the dataset, aiming to investigate their performance in emotion recognition. The results demonstrate that the fusion of multi-modal data significantly improves the accuracy of driver's emotion recognition, with accuracy increases of 11. 28% and 6. 83% compared to using only facial video signals or physiological signals, respectively. Therefore, the publication and analysis of multi-modal emotional data for driving scenarios is crucial to support further research in the fields of multimodal perception and intelligent transportation engineering.

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AAAI Conference 2024 Conference Paper

ResDiff: Combining CNN and Diffusion Model for Image Super-resolution

Shuyao Shang
Zhengyang Shan
Guangxing Liu
LunQian Wang
XingHua Wang
Zekai Zhang
Jinglin Zhang

Adapting the Diffusion Probabilistic Model (DPM) for direct image super-resolution is wasteful, given that a simple Convolutional Neural Network (CNN) can recover the main low-frequency content. Therefore, we present ResDiff, a novel Diffusion Probabilistic Model based on Residual structure for Single Image Super-Resolution (SISR). ResDiff utilizes a combination of a CNN, which restores primary low-frequency components, and a DPM, which predicts the residual between the ground-truth image and the CNN predicted image. In contrast to the common diffusion-based methods that directly use LR space to guide the noise towards HR space, ResDiff utilizes the CNN’s initial prediction to direct the noise towards the residual space between HR space and CNN-predicted space, which not only accelerates the generation process but also acquires superior sample quality. Additionally, a frequency-domain-based loss function for CNN is introduced to facilitate its restoration, and a frequency-domain guided diffusion is designed for DPM on behalf of predicting high-frequency details. The extensive experiments on multiple benchmark datasets demonstrate that ResDiff outperforms previous diffusion based methods in terms of shorter model convergence time, superior generation quality, and more diverse samples.

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