![]() Non-automatic methods, while providing good recovery results, require physically marking the damaged areas in the photos, taking a lot of time and effort. With their methods, in addition to the damaged photos as the input, additional damage masks should be specified before inputting into the model. proposed an inpainting model based on the generative adversarial network (GAN) and gated convolution. modified the confidence computation, strategy matching, and filling scheme to improve the inpainting method. The mentioned works focused on the design of repair methods. The semi-automatic method manually marks the damaged areas on the photos and then applies the inpainting methods to recover the contents of these locations. Manual recovery is made through a variety of image editing tools, such as Photoshop or GIMP, to recover damaged photos based on user knowledge. The non-automatic methods can be further subdivided into manual and semi-automatic methods. The existing recovery methods for damaged old photos can be divided into non-automatic and automatic processes according to whether human intervention is required. Fortunately, digital image processing technology can be applied to recover the content of these photos to its original state. Old photos can often contain various levels of damage caused by human improper storage or environmental factors that deteriorate the integrity of photos. The damage marking time is substantially reduced to less than 0.01 s per photo to speed up old photo recovery processing. Our experimental results show that our proposed damage detection model can detect complex damaged areas in old photos automatically and effectively. 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. ![]() Therefore, this paper proposes a deep learning-based architecture for automatically detecting damaged areas of old photos. 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. However, damage marking is a time-consuming and labor-intensive process. 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. ![]() Most methods for repairing damaged old photos are manual or semi-automatic. ![]()
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