Time-Travel Rephotography
원문 초록
Many historical people were only ever captured by old, faded, black and white photos, that are distorted due to the limitations of early cameras and the passage of time. This paper simulates traveling back in time with a modern camera to rephotograph famous subjects. Unlike conventional image restoration filters which apply independent operations like denoising, colorization, and superresolution, we leverage the StyleGAN2 framework to project old photos into the space of modern high-resolution photos, achieving all of these effects in a unified framework. A unique challenge with this approach is retaining the identity and pose of the subject in the original photo, while discarding the many artifacts frequently seen in low-quality antique photos. Our comparisons to current state-of-the-art restoration filters show significant improvements and compelling results for a variety of important historical people.
함께 살펴볼 논문
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Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models
EAMamba: Efficient All-Around Vision State Space Model for Image Restoration
Visual-Instructed Degradation Diffusion for All-in-One Image Restoration
TDiR: Transformer based Diffusion for Image Restoration Tasks
NAADA: A Noise-Aware Attention Denoising Autoencoder for Dental Panoramic Radiographs
Exploring Diffusion with Test-Time Training on Efficient Image Restoration
토론
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