4 citations · 5 across the 12 of their papers we have counts for
11 papers · 1 filter
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…
DEAL: Data-Efficient Adversarial Learning for High-Quality Infrared Imaging
Zhu Liu, Zijun Wang, Jinyuan Liu +3
Thermal imaging is often compromised by dynamic, complex degradations caused by hardware limitations and unpredictable environmental factors. The scarcity of high-quality infrared…
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…
Infrared and Visible Image Fusion: From Data Compatibility to Task Adaption
Jinyuan Liu, Guanyao Wu, Zhu Liu +6
Infrared-visible image fusion (IVIF) is a critical task in computer vision, aimed at integrating the unique features of both infrared and visible spectra into a unified representat…
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…
Towards Robust Image Stitching: An Adaptive Resistance Learning against Compatible Attacks
Zhiying Jiang, Xingyuan Li, Jinyuan Liu +2
Image stitching seamlessly integrates images captured from varying perspectives into a single wide field-of-view image. Such integration not only broadens the captured scene but al…
From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusion
Xingyuan Li, Yang Zou, Jinyuan Liu +4
With the rapid progression of deep learning technologies, multi-modality image fusion has become increasingly prevalent in object detection tasks. Despite its popularity, the inher…
ASF-Net: Robust Video Deraining via Temporal Alignment and Online Adaptive Learning
Xinwei Xue, Jia He, Long Ma +3
In recent times, learning-based methods for video deraining have demonstrated commendable results. However, there are two critical challenges that these methods are yet to address:…