Existing image fusion methods always use the same representations for different modal
medical images. Otherwise, they solve the fusion problem by subjectively defining …

Medical imaging has been widely used to diagnose various disorders over the past 20
years. Primary challenges in medicine include accurate disease identification and improved …

As an effective way to integrate the information contained in multiple medical images with
different modalities, medical image fusion has emerged as a powerful technique in various …

Medical image fusion technique plays an an increasingly critical role in many clinical
applications by deriving the complementary information from medical images with different …

Existing image fusion methods primarily focus on solving single-task fusion problems,
overlooking the potential information complementarity among multiple fusion tasks …

Image fusion is an important task for computer vision as a diverse range of applications are
benefiting from the fusion operation. The existing image fusion methods are largely …

The field of multimodal medical image fusion has made huge progress in the past decade.
However, previous methods always suffer from color distortion, blurring, and noise. To …

The main objective of image fusion for multimodal medical images is to retrieve valuable
information by combining multiple images obtained from various sources into a single image …

This paper proposes an effective infrared and visible image fusion method based on a
texture conditional generative adversarial network (TC-GAN). The constructed TC-GAN …

Multi-modal medical image fusion refers to the fusion of two or more medical images
obtained by different imaging methods into one image. Multi-modal medical images contain …