Preserving Old Memories in Vivid Detail: Human-Interactive Photo Restoration Framework

TL;DR

This paper introduces an AI-based photo restoration framework that automates and accelerates the process of restoring old photographs by addressing various damages through a multi-stage architecture, supported by a new dataset.

Contribution

It presents a novel multi-stage AI framework for photo restoration and introduces a new dataset for evaluating old photo restoration methods.

Findings

Effective restoration of damaged photographs demonstrated

Framework accelerates restoration process

New dataset enables better evaluation

Abstract

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.