The standard approach to tackling computer vision problems is to train deep convolutional
neural network (CNN) models using large-scale image datasets that are representative of …

Implicit neural representations (INRs) have emerged as a powerful paradigm in scene
reconstruction and computer graphics, showcasing remarkable results. By utilizing neural …

Medical image arbitrary-scale super-resolution (MIASSR) has recently gained widespread
attention, aiming to supersample medical volumes at arbitrary scales via a single model …

Magnetic resonance imaging (MRI) is a medical imaging technique used to show
anatomical structures and physiological processes of the human body. Due to limitations like …

Recently, several methods have explored the potential of multi-contrast magnetic resonance
imaging (MRI) super-resolution (SR) and obtain results superior to single-contrast SR …

Volumetric optical microscopy using non-diffracting beams enables rapid imaging of 3D
volumes by projecting them axially to 2D images but lacks crucial depth information …

In the realm of medical 3D data such as CT and MRI images prevalent anisotropic resolution
is characterized by high intra-slice but diminished inter-slice resolution. The lowered …

Sparse-view CT is a promising strategy for reducing the radiation dose of traditional CT
scans, but reconstructing high-quality images from incomplete and noisy data is challenging …

Implicit neural representation (INR) has surfaced as a promising direction for solving
different scientific visualization tasks due to its continuous representation and flexible input …

Compared to single-modality magnetic resonance (MR) image super-resolution (SR)
methods, multi-modality MR image methods can utilize high-resolution reference modality …