Yingkai Zhang — accepted AI-conference papers

2026

Enhancing Unregistered Hyperspectral Image Super-Resolution via Unmixing-based Abundance Fusion Learning

Yingkai Zhang, Tao Zhang, Jing Nie, Ying Fu

CVPR 2026

Unregistered hyperspectral image (HSI) super-resolution (SR) typically aims to enhance a low-resolution HSI using an unregistered high-resolution reference image. In this paper, we propose an unmixing-based fusion framework that decouples spatial-spectral information to simultaneously mitigate the i

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2026

Real Noise Decoupling for Hyperspectral Image Denoising

Yingkai Zhang, Tao Zhang, Jing Nie, Ying Fu

AAAI 2026technical

Hyperspectral image (HSI) denoising is a crucial step in enhancing the quality of HSIs. Noise modeling methods can fit noise distributions to generate synthetic HSIs to train denoising networks. However, the noise in captured HSIs is usually complex and difficult to model accurately, which significa

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