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Comparison of Methods for Image Inpainting based on Deep Learning
Rajsigl, Tomáš ; Herout, Adam (referee) ; Španěl, Michal (advisor)
This bachelor thesis aims to compare deep learning methods and approaches for image inpainting using quantitative metrics like PSNR, SSIM, and LPIPS. Moreover, a user study has also been carried out for further subjective assessment. For the purposes of this comparison, four GAN-based neural networks were used. The first network, AOT-GAN, represents a benchmark against which the proposed architecture and its modifications were compared. In the experiments, a variant of the proposed method achieved a 29% improvement against AOT-GAN in images with small missing regions. This claim is also supported by the results of the user study where this method was ranked as the best. As a result of this thesis, a small dataset specifically for the evaluation of image inpainting in the context of object removal was created. Real-world applications of these methods are demonstrated through a web application.
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