50 citations · 90 across the 6 of their papers we have counts for
6 papers
DeLightMono: Enhancing Self-Supervised Monocular Depth Estimation in Endoscopy by Decoupling Uneven Illumination
Mingyang Ou, Haojin Li, Yifeng Zhang +4
Self-supervised monocular depth estimation serves as a key task in the development of endoscopic navigation systems. However, performance degradation persists due to uneven illumin…
Endoscopic Depth Estimation Based on Deep Learning: A Survey
Ke Niu, Zeyun Liu, Xue Feng +5
Endoscopic depth estimation is a critical technology for improving the safety and precision of minimally invasive surgery. It has attracted considerable attention from researchers…
Fundus Image Quality Assessment and Enhancement: a Systematic Review
Heng Li, Haojin Li, Mingyang Ou +5
As an affordable and convenient eye scan, fundus photography holds the potential for preventing vision impairment, especially in resource-limited regions. However, fundus image deg…
AIF-SFDA: Autonomous Information Filter-driven Source-Free Domain Adaptation for Medical Image Segmentation
Haojin Li, Heng Li, Jianyu Chen +4
Decoupling domain-variant information (DVI) from domain-invariant information (DII) serves as a prominent strategy for mitigating domain shifts in the practical implementation of d…
Enhancing and Adapting in the Clinic: Source-free Unsupervised Domain Adaptation for Medical Image Enhancement
Heng Li, Ziqin Lin, Zhongxi Qiu +4
Medical imaging provides many valuable clues involving anatomical structure and pathological characteristics. However, image degradation is a common issue in clinical practice, whi…
A Generic Fundus Image Enhancement Network Boosted by Frequency Self-supervised Representation Learning
Heng Li, Haofeng Liu, Huazhu Fu +5
Fundus photography is prone to suffer from image quality degradation that impacts clinical examination performed by ophthalmologists or intelligent systems. Though enhancement algo…