The correction of exposure-related issues is a pivotal component in enhancing the quality of
images, offering substantial implications for various computer vision tasks. Historically, most …

In infrared and visible image fusion (IVIF), prior knowledge constraints established with
image-level information often ignore the identity and differences between source image …

The current constraint rules of end-to-end infrared and visible image fusion (IVIF) networks
based on deep learning solely focus on the pixel level, disregarding the consideration of …

Image fusion plays a key role in a variety of multi-sensor-based vision systems, especially
for enhancing visual quality and/or extracting aggregated features for perception. However …

Infrared and visible images captured by different devices can be seamlessly integrated into
a single composite image through the application of image fusion techniques. However …

Infrared and visible image fusion (IVF) aims to generate a fused image with abundant texture
details and salient thermal radiation targets, which can not only preserve the necessary …

In order to be adapted to high-level vision tasks, several infrared and visible image fusion
methods cascade with the downstream network to enhance the semantic information of …

Infrared and visible image fusion aims to generate a fused image by integrating and
distinguishing complementary information from multiple sources. While the cross-attention …

Achieving precise semantic segmentation for traffic scenes relies on adopting multi-spectral
image fusion techniques to attain high-quality images. Many existing fusion solutions often …

Infrared and visible image fusion (IVIF) aims to combine high contrast of infrared image and
rich texture details of visible image, which can break through the imaging limitations of …