15 citations · 28 across the 7 of their papers we have counts for
4 papers · 1 filter
HazeSpikeMamba: Coupling Spiking-Inspired and State-Space Features for Self-Supervised Real-World Dehazing
Haoran Liu, Huibin Li, Mingzhe Liu +2
Dehazing networks are commonly trained on synthetic hazy-clear pairs, but their performance often drops on real photographs. Synthetic haze generated using the atmospheric scatteri…
Ranking Infrared-Visible Fusion the Way Humans Do: A Learned Pairwise Preference Measure
Haoran Liu, Mingzhe Liu, Peng Li +1
Human pairwise comparison provides a direct basis for perceptual infrared-visible image fusion assessment, but dense annotation becomes costly as method pools grow. We present the…
U-Net-Like Spiking Neural Networks for Single Image Dehazing
Huibin Li, Haoran Liu, Mingzhe Liu +3
Image dehazing is a critical challenge in computer vision, essential for enhancing image clarity in hazy conditions. Traditional methods often rely on atmospheric scattering models…
Random-coupled Neural Network
Haoran Liu, Mingzhe Liu, Peng Li +4
Improving the efficiency of current neural networks and modeling them in biological neural systems have become popular research directions in recent years. Pulse-coupled neural net…