Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Department of Computer Science and Information Engineering, National Central University, Taoyuan City 32001, Taiwan.
Sensors (Basel). 2022 Nov 7;22(21):8580. doi: 10.3390/s22218580.
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.
大多数修复损坏旧照片的方法都是手动或半自动的。使用这些方法，首先必须手动标记损坏区域，以便稍后可以手动或通过算法进行修复。然而，损坏标记是一个耗时且劳动密集的过程。尽管有一些全自动的修复方法，但它们采用的是端到端修复的风格，这意味着它们无法控制损坏区域的检测，可能会破坏或无法完全保留有价值的历史照片。因此，本文提出了一种基于深度学习的架构，用于自动检测旧照片的损坏区域。我们设计了一个损坏检测模型，可以自动正确地标记照片中的损坏区域，然后可以使用任何现有的修复方法来修复这些损坏。我们的实验结果表明，我们提出的损坏检测模型可以自动有效地检测旧照片中的复杂损坏区域。损坏标记的时间大大减少到每张照片不到 0.01 秒，从而加快了旧照片恢复处理的速度。
