Songcheng Du — accepted AI-conference papers

2026

Pansharpening for Thin-Cloud Contaminated Remote Sensing Images: A Unified Framework and Benchmark Dataset

Songcheng Du, Yang Zou, Jiaxin Li, Mingxuan Liu, Ying Li, Changjing Shang +1

AAAI 2026technical

Pansharpening under thin cloudy conditions is a practically significant yet rarely addressed task, challenged by simultaneous spatial resolution degradation and cloud-induced spectral distortions. Existing methods often address cloud removal and pansharpening sequentially, leading to cumulative erro

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2026

UniFusion: A Unified Image Fusion Framework with Robust Representation and Source-Aware Preservation

Xingyuan Li, Songcheng Du, Yang Zou, Haoyuan Xu, Zhiying Jiang, Jinyuan Liu

CVPR 2026

Image fusion aims to integrate complementary information from multiple source images to produce a more informative and visually consistent representation, benefiting both human perception and downstream vision tasks. Despite recent progress, most existing fusion methods are designed for specific tas

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