Multi-modal medical image fusion (MMIF) integrates medical images of different modalities
into an image with rich information to boost the accuracy and efficiency of clinical diagnosis …

Multi-frame image super-resolution (MISR) aims to fuse information in low-resolution (LR)
image sequence to compose a high-resolution (HR) one, which is applied extensively in …

As a result of hardware resource constraints, it is difficult to obtain medical images with a
sufficient resolution to diagnose small lesions. Recently, super-resolution (SR) was …

Multi-modal image fusion is widely used in various fields, especially in military, medical,
industrial detection and other fields. Image fusion can integrate redundant and …

Image fusion aims to integrate multiple images of the same scene into an artificial image
which contains more useful information than any individual one. Due to the constraints of …

Multi-image super-resolution (MISR) usually outperforms single-image super-resolution
(SISR) under a proper inter-image alignment by explicitly exploiting the inter-image …

Background and objective: Magnetic resonance imaging can present the precise anatomic
structure in clinical applications. Nevertheless, due to the limited scanning equipment cost …

Limited by imaging systems, the reconstruction of Magnetic Resonance Imaging (MRI)
images from partial measurement is essential to medical imaging research. Benefiting from …

Current multi-contrast MRI super-resolution (SR) methods often harness convolutional
neural networks (CNNs) for feature extraction and fusion. However, existing models have …

There are two key requirements for medical lesion image super-resolution reconstruction in
intelligent healthcare systems: clarity and reality. Because only clear and real super …