Authors :
Berkan Höke
Volume/Issue :
Volume 6 - 2021, Issue 11 - November
Google Scholar :
http://bitly.ws/gu88
Scribd :
https://bit.ly/33AZWBf
Abstract :
A framework for image inpainting is presented
in this work. Inpainting is performed using discrete global
optimization problem with an objective function rather
than existing techniques. An iterative approach relies on
extended belief propagation, called Priority BP has been
used to solve the optimization. Although Priority-BP
provides serious speedup by reducing complexity,
inpainting an image is very intensive task. Hence, this
study aims to provide further speedup improvements by
using the power of GPUs through Compute Unified Device
Architecture (CUDA). The effect of parallel processing
framework is demonstrated on the real images for
inpainting tasks.
Keywords :
Image Inpainting, Image Restoration, GPU, CUDA.
A framework for image inpainting is presented
in this work. Inpainting is performed using discrete global
optimization problem with an objective function rather
than existing techniques. An iterative approach relies on
extended belief propagation, called Priority BP has been
used to solve the optimization. Although Priority-BP
provides serious speedup by reducing complexity,
inpainting an image is very intensive task. Hence, this
study aims to provide further speedup improvements by
using the power of GPUs through Compute Unified Device
Architecture (CUDA). The effect of parallel processing
framework is demonstrated on the real images for
inpainting tasks.
Keywords :
Image Inpainting, Image Restoration, GPU, CUDA.
