3 accepted papers
Multi-modality image fusion, particularly infrared and visible, plays a crucial role in integrating diverse modalities to enhance scene understanding. Although early research prioritized visual quality, preserving fine details and adapting to downstream tasks remains challenging. Recent approaches a…
Multi-exposure image fusion (MEF) has emerged as a prominent solution to address the limitations of digital imaging in representing varied exposure levels. Despite its advancements, the field grapples with challenges, notably the reliance on manual designs for network structures and loss functions,…
A series of infrared and visible image fusion (IVIF) methods have emerged to improve the performance of segmentation task. However, existing perception-focused IVIF methods take visual effects and semantic information as a unified goal for training, ignoring the task conflicts. Moreover, these metho…