Search Results for author:
Found 9 papers, 4 papers with code
Beyond Line of Sight Defense Communication Systems: Recent Advances and Future Challenges
Beyond Line of Sight (BLOS) communication stands as an indispensable element within defense communication strategies, facilitating information exchange in scenarios where traditional Line of Sight (LOS) methodologies encounter obstruction.
The Digitalization of Bioassays in the Open Research Knowledge Graph
For this in turn, there is a strong need for AI tools designed for scientists that permit easy and accurate semantification of their scholarly contributions.
Low Light Image Enhancement via Global and Local Context Modeling
These contexts can be crucial towards inferring several image enhancement tasks, e. g., local and global contrast, brightness and color corrections; which requires cues from both local and global spatial extent.
Image Super-Resolution using Explicit Perceptual Loss
The previous approaches use several loss functions which is hard to interpret and has the implicit relationships to improve the perceptual score.
Space-Time-Aware Multi-Resolution Video Enhancement
We consider the problem of space-time super-resolution (ST-SR): increasing spatial resolution of video frames and simultaneously interpolating frames to increase the frame rate.
Deep Back-Projection Networks for Single Image Super-resolution
Previous feed-forward architectures of recently proposed deep super-resolution networks learn the features of low-resolution inputs and the non-linear mapping from those to a high-resolution output.
Ranked #1 on
Image Super-Resolution
on BSDS100 - 8x upscaling
Recurrent Back-Projection Network for Video Super-Resolution
We proposed a novel architecture for the problem of video super-resolution.
Task-Driven Super Resolution: Object Detection in Low-resolution Images
We consider how image super resolution (SR) can contribute to an object detection task in low-resolution images.
Deep Back-Projection Networks For Super-Resolution
The feed-forward architectures of recently proposed deep super-resolution networks learn representations of low-resolution inputs, and the non-linear mapping from those to high-resolution output.
Ranked #16 on
Video Super-Resolution
on Vid4 - 4x upscaling
