Preserving Old Memories in Vivid Detail: Human-Interactive Photo Restoration Framework
This work provides a cost-effective and time-saving solution for individuals and organizations needing to preserve visual memories in old photos, though it is incremental as it builds on existing photo restoration techniques.
The paper tackles the problem of restoring old and deteriorated photographs by proposing an AI-based multi-stage framework that automates and accelerates the restoration process, addressing issues like physical damage and image quality loss. It also introduces a novel dataset for evaluation, as existing public datasets are lacking.
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.