TY - GEN
T1 - Preserving Old Memories in Vivid Detail
T2 - 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024
AU - Back, Seung Yeon
AU - Son, Geonho
AU - Jeong, Dahye
AU - Park, Eunil
AU - Woo, Simon S.
N1 - Publisher Copyright:
© 2024 Owner/Author.
PY - 2024/10/21
Y1 - 2024/10/21
N2 - Photo restoration technology enables preserving visual memories in photographs. However, physical prints are vulnerable to various forms of deterioration, ranging from physical damage to loss of image quality, etc. While restoration by human experts can improve the quality of outcomes, it often comes at a high price in terms of cost and time for restoration. In this work, we present the AI-based photo restoration framework composed of multiple stages, where each stage is tailored to enhance and restore specific types of photo damage, accelerating and automating the photo restoration process. By integrating these techniques into a unified architecture, our framework aims to offer a one-stop solution for restoring old and deteriorated photographs. Furthermore, we present a novel old photo restoration dataset because we lack a publicly available dataset for our evaluation.
AB - Photo restoration technology enables preserving visual memories in photographs. However, physical prints are vulnerable to various forms of deterioration, ranging from physical damage to loss of image quality, etc. While restoration by human experts can improve the quality of outcomes, it often comes at a high price in terms of cost and time for restoration. In this work, we present the AI-based photo restoration framework composed of multiple stages, where each stage is tailored to enhance and restore specific types of photo damage, accelerating and automating the photo restoration process. By integrating these techniques into a unified architecture, our framework aims to offer a one-stop solution for restoring old and deteriorated photographs. Furthermore, we present a novel old photo restoration dataset because we lack a publicly available dataset for our evaluation.
KW - computer vision
KW - photo restoration framework
UR - https://www.scopus.com/pages/publications/85210022109
U2 - 10.1145/3627673.3679215
DO - 10.1145/3627673.3679215
M3 - Conference contribution
AN - SCOPUS:85210022109
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 5180
EP - 5184
BT - CIKM 2024 - Proceedings of the 33rd ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
Y2 - 21 October 2024 through 25 October 2024
ER -