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cs.CV2026
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…
cs.CV2026
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…
cs.CV2025★ 2 cited
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…