Image fusion, which refers to extracting and then combining the most meaningful information
from different source images, aims to generate a single image that is more informative and …

Multiple imaging modalities can be combined to provide more information about the real
world than a single modality alone. Infrared images discriminate targets with respect to their …

This study proposes a novel general image fusion framework based on cross-domain long-
range learning and Swin Transformer, termed as SwinFusion. On the one hand, an attention …

The images captured by the cameras contain distortions, misclassified pixels, uncertainties
and poor contrast. Therefore, the multi-focus image fusion (MFIF) integrates various input …

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 …

In this paper, a new deep incomplete multi-view clustering network, called DIMC-net, is
proposed to address the challenge of multi-view clustering on missing views. In particular …

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 …

Due to the rapid development of artificial intelligence technology, industrial sectors are
revolutionizing in automation, reliability, and robustness, thereby significantly increasing …

Deep convolutional neural networks (CNNs) for image denoising have recently attracted
increasing research interest. However, plain networks cannot recover fine details for a …