Deep learning technologies have dramatically reshaped the field of medical image
registration over the past decade. The initial developments, such as regression-based and U …

In recent years, with the rapid development of deep learning, super-resolution methods
based on convolutional neural networks (CNNs) have made great progress. However, the …

Virtual try-on aims to generate a photo-realistic fitting result given an in-shop garment and a
reference person image. Existing methods usually build up multi-stage frameworks to deal …

Automatic surgical workflow recognition is a key component for develo** context-aware
computer-assisted systems in the operating theatre. Previous works either jointly modeled …

Images captured in clinic such as MRI scans are usually in 3D formats with high spatial
resolutions. Existing learning-based models for medical image registration consume large …

Motion estimation in echocardiography plays an important role in the characterization of
cardiac function, allowing the computation of myocardial deformation indices. However …

Subject motion is a long-standing problem of magnetic resonance imaging (MRI), which can
seriously deteriorate the image quality. Various prospective and retrospective methods have …

3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for
the assessment of cardiac function and the diagnosis of cardiovascular diseases. Current …

Deformation decomposition serves as a good solution for deformable image registration
when the deformation is large. Current deformation decomposition methods can be …

Cross-resolution face recognition (CRFR), which is important in intelligent surveillance and
biometric forensics, refers to the problem of matching a low-resolution (LR) probe face …