Modern MRI scanners have trended toward higher field strengths to maximize signal and
resolution while minimizing scan time. However, high‐field devices remain expensive to …

Denoising diffusion models have emerged as a powerful tool for various image generation
and editing tasks, facilitating the synthesis of visual content in an unconditional or input …

Recently, denoising diffusion probabilistic models (DDPM) have been applied to image
segmentation by generating segmentation masks conditioned on images, while the …

Denoising diffusion models have found applications in image segmentation by generating
segmented masks conditioned on images. Existing studies predominantly focus on adjusting …

Synthetic data have emerged as an attractive option for developing machine-learning
methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)—a …

Thanks to the recent achievements in task-driven image quality enhancement (IQE) models
like ESTR [1], the image enhancement model and the visual recognition model can mutually …

Many imaging inverse problems $\unicode {x2014} $ such as image-dependent in-painting
and dehazing $\unicode {x2014} $ are challenging because their forward models are …

Speckle noise significantly affects synthetic aperture radar (SAR) imaging systems, causing
difficulties in the post-processing of SAR images. To strike a balance between denoising …

Parcellation of mesh models for cortical analysis is a central problem in neuroimaging. Most
classical and deep learning methods have requisites in terms of mesh topology, requiring …

''Easy/hard sample” is a popular parlance in machine learning. Learning difficulty of samples
refers to how easy/hard a sample is during a learning procedure. An increasing need of …