Enhancement of damaged-image prediction based on digital twin technology

Abstract

AbstractDigital twins have revolutionized the field of image enhancement by applying their unique capabilities. A digital twin refers to a virtual replica of a physical object or system, which can be utilized to simulate and analyze real-world scenarios. In image enhancement, digital twins map entities to images, identify damaged areas, and restore them to their original state. This process involves utilizing the digital twin method to understand the underlying structure and characteristics of the image. The damaged areas can be accurately modeled and repaired using techniques like the Cahn-Hilliard equation. Additionally, neural network models are leveraged to measure the effectiveness of the image restoration process. Compared with the first-order numerical scheme, the second-order method can improve the prediction accuracy by more than 40% in some cases. Through these advancements, digital twins have significantly enhanced images’ quality, clarity, and visual appeal, contributing to various photography, healthcare, and remote sensing applications.