1 citations · 1 across the 9 of their papers we have counts for
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Unifying Amplitude-Phase Coupling and Reliable Endogenous Priors for Low-Light Image Enhancement
Tongshun Zhang, Zixuan Zhong, Pingping Liu +4
Frequency-domain methods have shown strong potential for low-light image enhancement (LLIE) because they can effectively regulate global illumination. However, most existing method…
RPCASSM: Robust PCA State Space Model For Infrared Small Target Detection
Pingping Liu, Aohua Li, Yubing Lu +3
The detection and segmentation of infrared small targets have important application significance in the fields of surveillance and security, maritime rescue and so on. Due to the l…
A Physically-Grounded Attack and Adaptive Defense Framework for Real-World Low-Light Image Enhancement
Tongshun Zhang, Pingping Liu, Yuqing Lei +3
Limited illumination often causes severe physical noise and detail degradation in images. Existing Low-Light Image Enhancement (LLIE) methods frequently treat the enhancement proce…
SPJFNet: Self-Mining Prior-Guided Joint Frequency Enhancement for Ultra-Efficient Dark Image Restoration
Tongshun Zhang, Pingling Liu, Zijian Zhang +1
Current dark image restoration methods suffer from severe efficiency bottlenecks, primarily stemming from: (1) computational burden and error correction costs associated with relia…
CIVQLLIE: Causal Intervention with Vector Quantization for Low-Light Image Enhancement
Tongshun Zhang, Pingping Liu, Zhe Zhang +1
Images captured in nighttime scenes suffer from severely reduced visibility, hindering effective content perception. Current low-light image enhancement (LLIE) methods face signifi…
Beyond Illumination: Fine-Grained Detail Preservation in Extreme Dark Image Restoration
Tongshun Zhang, Pingping Liu, Zixuan Zhong +2
Recovering fine-grained details in extremely dark images remains challenging due to severe structural information loss and noise corruption. Existing enhancement methods often fail…
CWNet: Causal Wavelet Network for Low-Light Image Enhancement
Tongshun Zhang, Pingping Liu, Yubing Lu +4
Traditional Low-Light Image Enhancement (LLIE) methods primarily focus on uniform brightness adjustment, often neglecting instance-level semantic information and the inherent chara…
BSMamba: Brightness and Semantic Modeling for Long-Range Interaction in Low-Light Image Enhancement
Tongshun Zhang, Pingping Liu, Mengen Cai +3
Current low-light image enhancement (LLIE) methods face significant limitations in simultaneously improving brightness while preserving semantic consistency, fine details, and comp…
Deep Metric Learning Assisted by Intra-variance in A Semi-supervised View of Learning
Liu Pingping, Liu Zetong, Lang Yijun +2
Deep metric learning aims to construct an embedding space where samples of the same class are close to each other, while samples of different classes are far away from each other.…