Author : Nermin Mohamed Fawzy Mahmoud Salem
CoAuthors : Hani M. K. Mahdi, and Hazem M. Abbas
Source : International Journal of Engineering and Advanced Technology (IJEAT)
Date of Publication : 08/2019
Abstract :
Over the past few years, Deep learning-based methods
have shown encouraging and inspiring results for one of the most
complex tasks of computer vision and image processing; Image
Inpainting. The difficulty of image inpainting is derived from its’
need to fully and deeply understand of the structure and texture of
images for producing accurate and visibly plausible results
especially for the cases of inpainting a relatively larger region.
Deep learning methods usually employ convolution neural
network (CNN) for processing and analyzing images using filters
that consider all image pixels as valid ones and usually use the
mean value to substitute the missing pixels. This result in artifacts
and blurry inpainted regions inconsistent with the rest of the
image. In this paper, a new novel-based method is proposed for
image inpainting of random-shaped missing regions with variable
size and arbitrary locations across the image. We employed the
use of dilated convolutions for composing multiscale context
information without any loss in resolution as well as including a
modification mask step after each convolution operation. The
proposed method also includes a global discriminator that also
considers the scale of patches as well as the whole image. The
global discriminator is responsible for capturing local continuity
of images texture as well as the overall global images’ features.
The performance of the proposed method is evaluated using two
datasets (Places2 and Paris Street View). Also, a comparison with
the recent state-of-the-art is preformed to demonstrate and prove
the effectiveness of our model in both qualitative and quantitative
evaluations.
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