9 accepted papers
Reconstructing hyperspectral images from compressive measurements is challenging due to a fundamental mismatch between locally reliable observations and globally entangled structures induced by spectral dispersion. This study formalizes this issue as a local–global dissonance in representation learn…
Recently, coded aperture snapshot spectral imaging (CASSI) has been actively researched to capture three-dimensional (3D) hyperspectral (HS) images for dynamic scenes, where the optical systems detect a 2D snapshot measurement while a computational algorithm performs the inverse problem for recoveri…
The spatial and spectral resolution trade-off in the hyperspectral imaging is a fundamental and essential issue, and automatically generating high-resolution images in both spatial and spectral domains (HR-HS) by merging a low spatial resolution hyperspectral (LR-HS) image and a high spatial resolut…
Hyperspectral imaging is a promising imaging modality, and has attracted increasing research attention by compressive sensing such as coded aperture snapshot spectral imaging (CASSI), for simultaneously capturing abundant information in spatial, spectral and temporal domains. Hyperspectral image (HS…
Deep convolutional neural networks (DCNN) have dominated the single image super resolution (SR) field, and demonstrated significant success in generating high-resolution (HR) images from the ideal low-resolution (LR) images captured under controlled imaging conditions. However, the resolved image pe…
Though U-Net has achieved tremendous success in medical image segmentation tasks, it lacks the ability to explicitly model long-range dependencies. Therefore, Vision Transformers have emerged as alternative segmentation structures recently, for their innate ability of capturing long-range correlatio…
Recently, a variety of vision transformers have been developed as their capability of modeling long-range dependency. In current transformer-based backbones for medical image segmentation, convolutional layers were replaced with pure transformers, or transformers were added to the deepest encoder to…
Coronavirus Disease 2019 (COVID-19) has rapidly spread in 2020, emerging a mass of studies for lung infection segmentation from CT images. Though many methods have been proposed for this issue, it is a challenging task because of infections of various size appearing in different lobe zones. To tackl…
Mitotic event detection is a fundamental step in investigating of cell behaviors. The event can be used to analyze various diseases, but most mitotic event detections performed previously focused only on two-dimensional (2D) images with time information. Owing to the complex background (normal cells…