13 citations · 52 across the 33 of their papers we have counts for
34 papers
Bridging Human Evaluation to Infrared and Visible Image Fusion
Jinyuan Liu, Xingyuan Li, Qingyun Mei +5
Infrared and visible image fusion (IVIF) integrates complementary modalities to enhance scene perception. Current methods predominantly focus on optimizing handcrafted losses and o…
Enhancing Infrared Vision: Progressive Prompt Fusion Network and Benchmark
Jinyuan Liu, Zihang Chen, Zhu Liu +4
We engage in the relatively underexplored task named thermal infrared image enhancement. Existing infrared image enhancement methods primarily focus on tackling individual degradat…
Every SAM Drop Counts: Embracing Semantic Priors for Multi-Modality Image Fusion and Beyond
Guanyao Wu, Haoyu Liu, Hongming Fu +4
Multi-modality image fusion, particularly infrared and visible, plays a crucial role in integrating diverse modalities to enhance scene understanding. Although early research prior…
DCEvo: Discriminative Cross-Dimensional Evolutionary Learning for Infrared and Visible Image Fusion
Jinyuan Liu, Bowei Zhang, Qingyun Mei +6
Infrared and visible image fusion integrates information from distinct spectral bands to enhance image quality by leveraging the strengths and mitigating the limitations of each mo…
Striving for Faster and Better: A One-Layer Architecture with Auto Re-parameterization for Low-Light Image Enhancement
Nan An, Long Ma, Guangchao Han +2
Deep learning-based low-light image enhancers have made significant progress in recent years, with a trend towards achieving satisfactory visual quality while gradually reducing th…
HUPE: Heuristic Underwater Perceptual Enhancement with Semantic Collaborative Learning
Zengxi Zhang, Zhiying Jiang, Long Ma +3
Underwater images are often affected by light refraction and absorption, reducing visibility and interfering with subsequent applications. Existing underwater image enhancement met…