Magnetic resonance imaging (MRI) can present multi-contrast images of the same
anatomical structures, enabling multi-contrast super-resolution (SR) techniques. Compared …

Multi-contrast magnetic resonance imaging (MRI) is the most common management tool
used to characterize neurological disorders based on brain tissue contrasts. However …

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

Deep learning-based image processing is capable of creating highly appealing results.
However, it is still widely considered as a “blackbox” transformation. In medical imaging, this …

Guided filter is a fundamental tool in computer vision and computer graphics, which aims to
transfer structure information from the guide image to the target image. Most existing …

In clinical practice, tri-modal medical image fusion, compared to the existing dual-modal
technique, can provide a more comprehensive view of the lesions, aiding physicians in …

The increasing availability of sensors enables the combination of a high-spatial-resolution
panchromatic image and a low-spatial-resolution multispectral image, which has become a …

With today's technology, various non-invasive imaging methods provide detailed insight into
the patient's anatomy and support the physician during surgery. Depending on the signal …

Image enhancement and recognition are two important applications in computer vision and
medical imaging. Current approaches to image enhancement and recognition work well …

With today's technology, various non-invasive imaging methods provide detailed insight into
the patient's anatomy and support the physician during surgery. Depending on the signal …