activity
20242026
most citedDMFourLLIE: Dual-Stage and Multi-Branch Fourier Network for Low-Light Image Enhancement

24 citations · 25 across the 7 of their papers we have counts for

collaborators

10 papers

cs.CV2026

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…

cs.CV2025

SpineBench: A Clinically Salient, Level-Aware Benchmark Powered by the SpineMed-450k Corpus

Ming Zhao, Wenhui Dong, Yang Zhang +23

Spine disorders affect 619 million people globally and are a leading cause of disability, yet AI-assisted diagnosis remains limited by the lack of level-aware, multimodal datasets.…

cs.CV2025

WEC-DG: Multi-Exposure Wavelet Correction Method Guided by Degradation Description

Ming Zhao, Pingping Liu, Tongshun Zhang +1

Multi-exposure correction technology is essential for restoring images affected by insufficient or excessive lighting, enhancing the visual experience by improving brightness, cont…

cs.CV20251 cited

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…

cs.CV2025

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…

cs.CV2025

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…