Lin Wang

Event cameras sense intensity changes and have many advantages over conventional cameras. To take advantage of event cameras, some methods have been proposed to reconstruct intensity images from event streams. However, the outputs are still in low resolution (LR), noisy, and unrealistic. The low-quality outputs stem broader applications of event cameras, where high spatial resolution (HR) is needed as well as high temporal resolution, dynamic range, and no motion blur. We consider the problem of

High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range of exposures, which is important in image processing, computer graphics, and computer vision. In recent years, there has been a significant advancement in HDR imaging using deep learning (DL). This study conducts a comprehensive and insightful survey and analysis of recent developments in deep HDR imaging methodologies. We hierarchically and structurally group existing deep HDR imaging methods into five catego

Event cameras sense per-pixel intensity changes and produce asynchronous event streams with high dynamic range and less motion blur, showing advantages over the conventional cameras. A hurdle of training event-based models is the lack of large qualitative labeled data. Prior works learning end-tasks mostly rely on labeled or pseudo-labeled datasets obtained from the active pixel sensor (APS) frames; however, such datasets’ quality is far from rivaling those based on the canonical images. In this

Event cameras are novel sensors that perceive the perpixel intensity changes and output asynchronous event streams with high dynamic range and less motion blur. It has been shown that events alone can be used for end-task learning, e.g., semantic segmentation, based on encoder-decoder-like networks. However, as events are sparse and mostly reflect edge information, it is difficult to recover original details merely relying on the decoder. Moreover, most methods resort to the pixel-wise loss alon

We demonstrate that single-layer graphene (SLG) decorated with a high density of Ag adatoms displays the unconventional phenomenon of negative quantum capacitance. The Ag adatoms act as resonant impurities and form nearly dispersionless resonant impurity bands near the charge neutrality point (CNP). Resonant impurities quench the kinetic energy and drive the electrons to the Coulomb energy dominated regime with negative compressibility. In the absence of a magnetic field, negative quantum capaci

Event cameras sense brightness changes in each pixel and yield asynchronous event streams instead of producing intensity images. They have distinct advantages over conventional cameras, such as a high dynamic range (HDR) and no motion blur. To take advantage of event cameras with existing image-based algorithms, a few methods have been proposed to reconstruct images from event streams. However, the output images have a low resolution (LR) and are unrealistic. Low-quality outputs stem from broade

We report the structure characterization and electronic property modification of single layer graphene (SLG) field-effect transistor (FET) devices top-gated using ultrathin Y(2)O(3) as dielectric layers. Based on the Boltzmann transport theory within variant screening, Coulomb scattering is confirmed quantitatively to be dominant in Y(2)O(3)-covered SLG and a very few short-range impurities have been introduced by Y(2)O(3). Both DC transport and AC capacitance measurements carried out at cryogen