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Found 6 papers, 3 papers with code
OpenRR-1k: A Scalable Dataset for Real-World Reflection Removal
Following this paradigm, we collect a Real-world, Diverse, and Pixel-aligned dataset (named OpenRR-1k dataset), which contains 1, 000 high-quality transmission-reflection image pairs collected in the wild.
VIP: Video Inpainting Pipeline for Real World Human Removal
Inpainting for real-world human and pedestrian removal in high-resolution video clips presents significant challenges, particularly in achieving high-quality outcomes, ensuring temporal consistency, and managing complex object interactions that involve humans, their belongings, and their shadows.
Survey on Single-Image Reflection Removal using Deep Learning Techniques
The phenomenon of reflection is quite common in digital images, posing significant challenges for various applications such as computer vision, photography, and image processing.
Futuristic Variations and Analysis in Fundus Images Corresponding to Biological Traits
Our proposed DL models, named FAG-Net and FGC-Net, correspondingly estimate biological traits (age and gender) and generates fundus images.
Real-Time Super-Resolution for Real-World Images on Mobile Devices
Image Super-Resolution (ISR), which aims at recovering High-Resolution (HR) images from the corresponding Low-Resolution (LR) counterparts.
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
