Learning-Based Image Damage Area Detection for Old Photo Recovery

Tien Ying Kuo, Yu Jen Wei, Po Chyi Su, Tzu Hao Lin

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Most methods for repairing damaged old photos are manual or semi-automatic. With these methods, the damaged region must first be manually marked so that it can be repaired later either by hand or by an algorithm. However, damage marking is a time-consuming and labor-intensive process. Although there are a few fully automatic repair methods, they are in the style of end-to-end repairing, which means they provide no control over damaged area detection, potentially destroying or being unable to completely preserve valuable historical photos to the full degree. Therefore, this paper proposes a deep learning-based architecture for automatically detecting damaged areas of old photos. We designed a damage detection model to automatically and correctly mark damaged areas in photos, and this damage can be subsequently repaired using any existing inpainting methods. Our experimental results show that our proposed damage detection model can detect complex damaged areas in old photos automatically and effectively. The damage marking time is substantially reduced to less than 0.01 s per photo to speed up old photo recovery processing.

Original languageEnglish
Article number8580
JournalSensors (Switzerland)
Volume22
Issue number21
DOIs
StatePublished - Nov 2022

Keywords

  • damage area detection
  • damaged old photo
  • deep learning

Fingerprint

Dive into the research topics of 'Learning-Based Image Damage Area Detection for Old Photo Recovery'. Together they form a unique fingerprint.

Cite this