Medical image fusion has brought practical applications in clinical diagnosis. However,
image fusion methods still face challenges because of problems with the quality of the input …

Medical image fusion has many applications to healthcare that is accomplished by
extracting and then combining the complementary information from multiple medical images …

This paper proposes a new framework for leak diagnosis in pipelines using leak-augmented
scalograms and deep learning. Acoustic emission (AE) scalogram images obtained from the …

Abstract The Generative Adversarial Network (GAN) is commonly used for medical image
augmentation, a method to alleviate the data shortage for downstream tasks. However …

Medical image fusion is the process of combining information from multiple medical images
of the same body region acquired using different imaging modalities, such as computed …

One of the approaches to improve the performance of image processing applications is to
enhance the input image quality. Some common problems with medical images include lack …

Existing image fusion methods always ignore complementary features and saliency from
different inputs. To address these limitations, this paper proposes an unsupervised multi …

The advancement of medical imaging has led to the acquisition of image data from multiple
modalities, necessitating the development of robust algorithms for accurate and reliable …

Medical image fusion plays a vital role in clinical applications by combining complementary
information from different imaging modalities, such as MRI, CT, PET, and SPECT, into a …

The importance of accurate clinical diagnosis has garnered that for scholars within a
particular field of medical imaging, leading to a focus on image fusion as a potential …