In recent years, deep learning-based methods have emerged as dominant players in the
field of super-resolution (SR), owing to their exceptional reconstruction performance. The …

Background: In the field of medical imaging, high-resolution (HR) magnetic resonance
imaging (MRI) is essential for accurate disease diagnosis and analysis. However, HR …

The single image super-resolution (SISR) task has received much attention due to the wide
range of applications in many tasks. The progress in this SISR is mainly based on deep …

Diffusion Models (DMs) are probabilistic models that create realistic samples by simulating
the diffusion process, gradually adding and removing noise from data. These models have …

Existing deep learning-based single-image super-resolution (SR) methods typically rely on
vast quantities of paired data. As an essential solution, zero-shot SR methods require only a …

Diffusion models (DMs) are a type of potential generative models, which have achieved
better effects in many fields than traditional methods. DMs consist of two main processes …

The significant gap between the assumed and actual degradation model will undoubtedly
lead to a serious performance decrease for deep learning based image super-resolution …

Spike camera is a retina-inspired neuromorphic camera which can capture dynamic scenes
of high-speed motion by firing a continuous stream of spikes at an extremely high temporal …

We present X-MDPT (Cross-view Masked Diffusion Prediction Transformers), a novel
diffusion model designed for pose-guided human image generation. X-MDPT distinguishes …

Light Field (LF) imaging captures the spatial and angular information of light rays in the real
world and enables various applications, including digital refocusing and single-shot depth …