Search Results for author:
Found 6 papers, 3 papers with code
Deep learning-based blind image super-resolution with iterative kernel reconstruction and noise estimation
Yet, there is a gap in the literature to provide a well-generalized deep learning-based solution that performs well on images with unknown and highly complex degradations.
HSTR-Net: Reference Based Video Super-resolution for Aerial Surveillance with Dual Cameras
Aerial surveillance requires high spatio-temporal resolution (HSTR) video for more accurate detection and tracking of objects.
HSTR-Net: High Spatio-Temporal Resolution Video Generation For Wide Area Surveillance
Wide area surveillance has many applications and tracking of objects under observation is an important task, which often needs high spatio-temporal resolution (HSTR) video for better precision.
Altitude Optimization of UAV Base Stations from Satellite Images Using Deep Neural Network
It is expected that unmanned aerial vehicles (UAVs) will play a vital role in future communication systems.
HM-Net: A Regression Network for Object Center Detection and Tracking on Wide Area Motion Imagery
Wide Area Motion Imagery (WAMI) yields high-resolution images with a large number of extremely small objects.
Multi-hypothesis contextual modeling for semantic segmentation
Instead of using a fixed segmentation and neighborhood definition, we explore various contextual models for fusion of complementary information available in alternative segmentations of the same image.
