Abstract— Digital images can experience various disturbances
in acquisition and storage, one of which is a disturbance indicated
by damage to certain areas of the image field and causes the loss
of some of the information represented by the image. One of the
ways to restore an image experiencing disturbances like this is with
image completion technology. Image completion is an image
restoration technology capable of filling in or completing missing
or corrupted parts of an image. Various methods have been
developed for this image completion, starting from those based on
basic image processing to the latest relying on artificial intelligence
algorithms. This study aims to develop and implement an image
completion model based on deep learning with the transfer
learning method from the completion.net architecture. Using the
Facesrub training dataset consisting of a collection of unique facial
photos allows the model to understand facial attributes better.
Compared to conventional image completion based on image
patches, the method developed in this study can perform image
filling in image gaps with more realistic results. Based on visual
tests conducted on respondents, the results obtained enable
respondents to understand all the information represented by the
restored image, similar to the original image.