Adversarial Training for Physics-Informed Neural Networks. - Yao Li; Shengzhu Shi; Zhichang Guo; Boying Wu (CoRR, 2023)

Exploring The Robustness Of Deep Image Despeckling Models in an Adversarial Perspective. - Jie Ning; Yao Li; Zhichang Guo; Jiebao Sun; Shengzhu Shi; Enzhe Zhao; Boying Wu (IGARSS, 2024)

Fractional-order cross-diffusion system for multiplicative noise removal. - Juanjuan Gao; Jiebao Sun; Shengzhu Shi (Comput. Math. Appl., 2024)

Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling. - Jie Ning; Jiebao Sun; Shengzhu Shi; Zhichang Guo; Yao Li; Hongwei Li; Boying Wu (CoRR, 2024)

Boosting the Generalization Ability for Hyperspectral Image Classification Using Spectral-Spatial Axial Aggregation Transformer. - Enzhe Zhao; Zhichang Guo; Shengzhu Shi; Yao Li; Jia Li; Dazhi Zhang (IEEE Trans. Geosci. Remote. Sens., 2024)

Re-initialization-Free Level Set Method via Molecular Beam Epitaxy Equation Regularization for Image Segmentation. - Fanghui Song; Jiebao Sun; Shengzhu Shi; Zhichang Guo; Dazhi Zhang (J. Math. Imaging Vis., 2024)

Diffusion probabilistic multi-cue level set for reducing edge uncertainty in pancreas segmentation. - Yue Gou; Yuming Xing; Shengzhu Shi; Zhichang Guo (Biomed. Signal Process. Control., 2025)

Efficient SAV-Based Algorithms for Image Multiplicative Denoising with a Second Fundamental Form Regularizer. - Ziqing Ma; Chang Yang; Shengzhu Shi; Zhichang Guo; Wenjuan Yao; Bibo Lu (J. Sci. Comput., 2025)