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Zhen Zhang

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65 papers

2 author rows

AAAI Conference 2026 Conference Paper

A More Efficient Reduction from Outlier-Aware to Outlier-Free k-Median

Zhen Zhang
Han Peng
Limei Liu
Junyu Huang
Xiaolong Li
Qilong Feng

Given a non-negative integer \ell, the k-median with outliers problem extends the standard k-median problem by allowing the removal of up to \ell points and minimizing the clustering cost over the remaining ones. Algorithmic development in this setting remains an active area of research due to its relevance in processing noisy data. In this paper, we present a sampling-based reduction from the k-median with outliers problem to its outlier-free counterpart. The reduction incurs a multiplicative overhead of (kℓ⁻¹ + ε⁻¹)^O(ℓ) in the running time: it yields (kℓ⁻¹ + ε⁻¹)^O(ℓ) outlier-free instances, a solution to one of which can be directly transformed into a solution to the original instance with an arbitrarily small loss in the approximation ratio. This improves upon the previously known reduction with an overhead of ((k + ℓ)ε⁻¹)^O(ℓ). As applications, we obtain faster fixed-parameter tractable (FPT) algorithms with tight approximation guarantees for the k-median with outliers problem under various metric spaces. Furthermore, our approach naturally generalizes to constrained variants of the problem where additional constraints are imposed on the cluster sizes, and yields similar improvements in their FPT approximations.

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JBHI Journal 2026 Journal Article

A Self-Supervised Diffusion Model With Edge Prior for Unpaired LDCT Denoising

Zhen Zhang
Huizhen Zhang
Shaohua Zheng
Lin Pan
Mingjing Yang
Liqin Huang
Qiang Wu
Zhiyong Zhang

Low-dose computed tomography (LDCT) reduces health risks from radiation exposure but introduces imaging noise and artifacts. While numerous studies have employed deep learning for LDCT image denoising, the field continues to face significant challenges. Recent advancements have seen diffusion models applied to overcome issues of over-smoothness and unstable training inherent in prior deep learning approaches. However, the diffusion models face challenges in direct practical applications due to the extensive sampling steps, significant inference time required, and the need for hard-to-obtain paired data during training. To address these difficulties, this paper introduces a self-supervised diffusion model with edge prior for unpaired LDCT denoising. This method enables denoising within a lower-dimensional space, reducing computational complexity. Our proposed approach enhances denoised image clarity by applying prior edge constraints to compressed encodings; it employs a noise-conditioned encoding strategy to facilitate self-supervised image training, enabling the method to be applicable to unpaired CT data; and it utilizes compressed LDCT encoding as intermediate sampling results during the inference process, thereby accelerating sampling and reducing the time required for inference, making the method more real-time capable. Extensive validation across multiple datasets demonstrates that our method achieves competitive performance against state-of-the-art approaches in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and perceptual quality (LPIPS), while maintaining a practically acceptable inference time.