Zishu Yao — accepted AI-conference papers

2026

BiEvLight: Bi-level Learning of Task-Aware Event Refinement for Low-Light Image Enhancement

Zishu Yao, Xiang-Xiang Su, Shengning Zhou, Guang-Yong Chen, Guodong Fan, Xing Chen

CVPR 2026

Event cameras, with their high dynamic range, show great promise for Low-light Image Enhancement (LLIE). Existing works primarily focus on designing effective modal fusion strategies. However, a key challenge is the dual degradation from intrinsic background activity (BA) noise in events and low sig

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2025

IniRetinex: Rethinking Retinex-type Low-Light Image Enhancer via Initialization Perspective

Guodong Fan, Zishu Yao, Guang-Yong Chen, Jian-Nan Su, Min Gan

AAAI 2025technical

Retinex-based methods have become a general approach for solving low-light image enhancement (LLIE). However, traditional methods require post-processing of illumination (e.g., gamma correction), which lacks adaptability and disrupts the illumination structure. Retinex-based deep networks typically…

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