Articles tagged with Inpainting
Researchers at Kaunas University of Technology developed an end-to-end system to eliminate visual distractions caused by overlapping camera angles in live football broadcasts. The system uses YOLOv8 object detection and video inpainting technology to remove unwanted objects, such as cameramen, from frames.
A team of researchers at Osaka University has created a machine learning system that can virtually remove buildings from a live view, streaming in real-time on a mobile device. This technology can help accelerate the process of urban renewal based on community agreement, reducing conflicts and delays.
A team of researchers at Osaka University created a custom dataset to train an AI algorithm to digitally remove unwanted objects from building façade images. The algorithm achieved high accuracy in inpainting occluded regions with digital inpainting.
Researchers from Skoltech and KU Leuven used machine learning to reconstruct 3D micro-CT images of fibrous materials, overcoming the difficulties faced by humans in analyzing these complex materials. The team employed GANs to fill a gap in available inpainting tools, enabling precise material analysis and simulation.
Researchers at the University of Tokyo have developed a frequency-based inpainting method that enables the use of both frequency and spatial information to generate realistic missing image portions. The technique outperforms state-of-the-art methods in quality metrics such as PSNR, SSIM, and L1.
Scientists from the University of Minnesota and Duke University developed computer techniques that can automate image inpainting, restoring lost regions with neighboring available information. This technology has applications in surveillance images, enabling faster recovery of lost information and potential super resolution.