Visible and infrared image fusion (VIF) has attracted a lot of interest in recent years due to its
application in many tasks, such as object detection, object tracking, scene segmentation …

Multimodal medical image fusion consists in combining two or more images of the same or
different modalities aiming to improve the image content, and preserve information. The …

This study proposes a novel unified and unsupervised end-to-end image fusion network,
termed as U2Fusion, which is capable of solving different fusion problems, including multi …

Infrared and visible image fusion is aims to generate a composite image that can
simultaneously describe the salient target in the infrared image and texture details in the …

The existing deep learning fusion methods mainly concentrate on convolutional neural
networks (CNNs), and few attempts are made with transformer. Meanwhile, the …

Poor weather conditions, such as fog, haze, and mist, cause visibility degradation in
captured images. Existing imaging devices lack the ability to effectively and efficiently …

Multi-focus image fusion is an effective technique to extend the depth-of-field of optical
lenses by creating an all-in-focus image from a set of partially focused images of the same …

Multi-focus image fusion (MFIF) is an important area in image processing. Since 2017, deep
learning has been introduced to the field of MFIF and various methods have been proposed …

Existing image fusion methods are typically limited to aligned source images and have to
“tolerate” parallaxes when images are unaligned. Simultaneously, the large variances …

Image manipulation detection is different from traditional semantic object detection because
it pays more attention to tampering artifacts than to image content, which suggests that richer …