8 papers
Establishing Stochastic Object Models from Noisy Data via Ambient Measurement-Integrated Diffusion
Xiaoning Lei, Jianwei Sun, Wenhao Cai +3
Task-based measures of image quality (IQ) are critical for evaluating medical imaging systems, which must account for randomness including anatomical variability. Stochastic object…
FAST: Foreground-aware Diffusion with Accelerated Sampling Trajectory for Segmentation-oriented Anomaly Synthesis
Xichen Xu, Yanshu Wang, Jinbao Wang +4
Industrial anomaly segmentation relies heavily on pixel-level annotations, yet real-world anomalies are often scarce, diverse, and costly to label. Segmentation-oriented industrial…
From Physical Degradation Models to Task-Aware All-in-One Image Restoration
Hu Gao, Xiaoning Lei, Xichen Xu +2
All-in-one image restoration aims to adaptively handle multiple restoration tasks with a single trained model. Although existing methods achieve promising results by introducing pr…
A Survey on Industrial Anomalies Synthesis
Yanshu Wang, Xichen Xu, Jiaqi Liu +4
This paper comprehensively reviews anomaly synthesis methodologies. Existing surveys focus on limited techniques, missing an overall field view and understanding method interconnec…
SARD: Segmentation-Aware Anomaly Synthesis via Region-Constrained Diffusion with Discriminative Mask Guidance
Yanshu Wang, Xichen Xu, Xiaoning Lei +1
Synthesizing realistic and spatially precise anomalies is essential for enhancing the robustness of industrial anomaly detection systems. While recent diffusion-based methods have…
Physically Interpretable Multi-Degradation Image Restoration via Deep Unfolding and Explainable Convolution
Hu Gao, Xiaoning Lei, Xichen Xu +2
Although image restoration has advanced significantly, most existing methods target only a single type of degradation. In real-world scenarios, images often contain multiple degrad…