| dc.description.abstract | The widespread use of street view imagery in various applications, such as urban planning, navigation,
and real estate, has raised concerns about the privacy of individuals captured in these images. While
anonymization methods, such as blurring or pixelation, are commonly used to address these concerns, they
often result in a loss of important information and can be easily circumvented by determined individuals.
This thesis aims to explore an alternative approach to protecting privacy in street view imagery by using
image inpainting techniques. Image inpainting involves filling in missing or obscured regions of an image
in a way that preserves the overall structure and context of the scene. The proposed approach will utilize
conditional Generative Adversarial Networks (cGANs) for the purpose of image inpainting, specifically in
the context of street view images where people have been removed. While current cGAN-based methods
rely solely on incomplete images with missing areas, our approach incorporates an additional source
of information in the form of a semantic segmentation map. The semantic segmentation map serves
as a prior to guide the inpainting process, allowing the model to better understand the context and
structure of the image. By utilizing both the incomplete image and the semantic segmentation map, we
aim to improve the quality and realism of the inpainted results. We compared the performance of our
two proposed models, SemGAN and SemGAN-GT, with the existing pix2pix method. SemGAN utilizes
inpainted semantic segmentation maps as a prior in image inpainting, while SemGAN-GT uses ground
truth semantic segmentation maps. We evaluated the performance of each method using established
image quality metrics, such as L1, SSIM, and PSNR, as well as a qualitative analysis of the inpainted
images. Our findings indicate that both SemGAN and SemGAN-GT outperform the pix2pix method in
terms of image quality and realism. This suggests that semantic information improves the quality and
realism of the inpainted results, based on our quantitative results and qualitative analysis. | |
