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 …

By integrating the information contained in multiple images of the same scene into one
composite image, pixel-level image fusion is recognized as having high significance in a …

In this paper, we propose a general image fusion framework based on the convolutional
neural network, named as IFCNN. Inspired by the transform-domain image fusion …

In this paper, we propose a fast unified image fusion network based on proportional
maintenance of gradient and intensity (PMGI), which can end-to-end realize a variety of …

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 …

As is well known, activity level measurement and fusion rule are two crucial factors in image
fusion. For most existing fusion methods, either in spatial domain or in a transform domain …

As a popular signal modeling technique, sparse representation (SR) has achieved great
success in image fusion over the last few years with a number of effective algorithms being …

Pixel-level image fusion is designed to combine multiple input images into a fused image,
which is expected to be more informative for human or machine perception as compared to …

Multi-focus image fusion (MFF) is an effective way to eliminate the out-of-focus blur
generated in the imaging process. The difficulties in distinguishing different blur levels and …

Intelligent techniques foster the dissemination of new discoveries and novel technologies
that advance the ability of robots to assist and support humans. The human-centered …