In this paper, we proposed a new end-to-end model, termed as dual-discriminator
conditional generative adversarial network (DDcGAN), for fusing infrared and visible images …

In this paper, we propose a novel end-to-end model for fusing medical images
characterizing structural information, ie, IS, and images characterizing functional information …

Medical image fusion increases accuracy of clinical diagnosis and analysis through
integrating complementary information of multi-modality medical images. A novel multi …

Off late, medical image fusion has emerged as an inspiring approach in merging different
modalities of medical images. The fused image helps the medicos to diagnose various …

Multimodality medical image fusion is the important area in the medical imaging field which
enhances the reliability of medical diagnosis. Medical image fusion as well as their …

In this paper, we propose the medical Wasserstein generative adversarial networks
(MWGAN), an end-to-end model, for fusing magnetic resonance imaging (MRI) and positron …

Fusion is a critical step in image processing tasks. Recently, deep learning networks have
been considerably applied in information fusion. But the significant limitation of existing …

Most of the existing image fusion methods fail to retain sufficient salient information, lack
focuses on the most discriminative regions of the image, and often neglect the subjective …

Two main factors motivate the need for color in image processing. First, color is a strong
descriptor frequently simplifying the recognition and extraction of objects from a picture …

The fused functional and anatomical image provides additional diagnostic information
essential for precise diagnosis of disease and treatment planning. This work focused on …