Characterizing the Immaterial. Noninvasive Imaging and Analysis of
Stephen Benton's Hologram Engine no. 9
- URL: http://arxiv.org/abs/2110.06080v1
- Date: Tue, 12 Oct 2021 15:33:23 GMT
- Title: Characterizing the Immaterial. Noninvasive Imaging and Analysis of
Stephen Benton's Hologram Engine no. 9
- Authors: Marc Walton, Pengxiao Hao, Marc Vermeulen, Florian Willomitzer, Oliver
Cossairt
- Abstract summary: holography is a unique merging of art and technology. Invented in 1962, holography is a unique merging of art and technology.
Today, holography is experiencing new interest as analog holograms enter major museum collections as bona fide works of art.
- Score: 6.305493768841699
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Invented in 1962, holography is a unique merging of art and technology. It
persisted at the scientific cutting edge through the 1990s, when digital
imaging emerged and supplanted film. Today, holography is experiencing new
interest as analog holograms enter major museum collections as bona fide works
of art. In this essay, we articulate our initial steps at Northwestern's Center
for Scientific Studies in the Arts to describe the technological challenges on
the conservation of holograms, emphasizing their nature as an active material.
A holographic image requires user interaction to be viewed, and the materials
are delicate and prone to deterioration. Specifically, we outline our methods
for creating digital preservation copies of holographic artworks by documenting
the wavefront of propagating light. In so doing, we demonstrate why it remains
challenging to faithfully capture their high spatial resolution, the full
parallax, and deep depths of field without terabytes of data. In addition, we
use noninvasive analytical techniques such as spectral imaging, X-ray
fluorescence, and optical coherence tomography, to provide insights on hologram
material properties. Through these studies we hope to address current concerns
about the long term preservation of holograms while translating this artform
into a digital format to entice new audiences.
Related papers
- Graphics4Science: Computer Graphics for Scientific Impacts [69.54528197718207]
This course explores the relationship between computer graphics and science.<n>We show how core methods, such as geometric reasoning and physical modeling, provide inductive biases that help address challenges in both fields.<n>We aim to reframe graphics as a modeling language for science by bridging vocabulary gaps between the two communities.
arXiv Detail & Related papers (2025-06-18T18:06:58Z) - Speaking images. A novel framework for the automated self-description of artworks [0.6138671548064356]
Recent breakthroughs in generative AI have opened the door to new research perspectives in the domain of art and cultural heritage.<n>We propose a new framework towards the production of self-explaining cultural artifacts using open-source large-language, face detection, text-to-speech and audio-to-animation models.
arXiv Detail & Related papers (2025-05-28T09:13:41Z) - Solutions to Deepfakes: Can Camera Hardware, Cryptography, and Deep Learning Verify Real Images? [51.3344199560726]
It is imperative to establish methods that can separate real data from synthetic data with high confidence.
This document aims to: present known strategies in detection and cryptography that can be employed to verify which images are real.
arXiv Detail & Related papers (2024-07-04T22:01:21Z) - Holo-VQVAE: VQ-VAE for phase-only holograms [1.534667887016089]
Holography stands at the forefront of visual technology innovation, offering immersive, three-dimensional visualizations through the manipulation of light wave amplitude and phase.
Modern research in hologram generation has predominantly focused on image-to-hologram conversion, producing holograms from existing images.
We present Holo-VQVAE, a novel generative framework tailored for phase-only holograms (POHs)
arXiv Detail & Related papers (2024-03-29T15:27:28Z) - Configurable Learned Holography [33.45219677645646]
We introduce a learned model that interactively computes 3D holograms from RGB-only 2D images for a variety of holographic displays.
We enable our hologram computations to rely on identifying the correlation between depth estimation and 3D hologram synthesis tasks.
arXiv Detail & Related papers (2024-03-24T13:57:30Z) - Perceptual Artifacts Localization for Image Synthesis Tasks [59.638307505334076]
We introduce a novel dataset comprising 10,168 generated images, each annotated with per-pixel perceptual artifact labels.
A segmentation model, trained on our proposed dataset, effectively localizes artifacts across a range of tasks.
We propose an innovative zoom-in inpainting pipeline that seamlessly rectifies perceptual artifacts in the generated images.
arXiv Detail & Related papers (2023-10-09T10:22:08Z) - Text-Guided Synthesis of Eulerian Cinemagraphs [81.20353774053768]
We introduce Text2Cinemagraph, a fully automated method for creating cinemagraphs from text descriptions.
We focus on cinemagraphs of fluid elements, such as flowing rivers, and drifting clouds, which exhibit continuous motion and repetitive textures.
arXiv Detail & Related papers (2023-07-06T17:59:31Z) - FigGen: Text to Scientific Figure Generation [9.091505857494681]
We introduce the problem of text-to-figure generation, that is creating scientific figures of papers from text descriptions.
We present FigGen, a diffusion-based approach for text-to-figure as well as the main challenges of the proposed task.
arXiv Detail & Related papers (2023-06-01T15:28:41Z) - Learning to Evaluate the Artness of AI-generated Images [64.48229009396186]
ArtScore is a metric designed to evaluate the degree to which an image resembles authentic artworks by artists.
We employ pre-trained models for photo and artwork generation, resulting in a series of mixed models.
This dataset is then employed to train a neural network that learns to estimate quantized artness levels of arbitrary images.
arXiv Detail & Related papers (2023-05-08T17:58:27Z) - Image quality enhancement of embedded holograms in holographic
information hiding using deep neural networks [0.0]
The brightness of an embedded hologram is set to a fraction of that of the host hologram, resulting in a barely damaged reconstructed image of the host hologram.
It is difficult to perceive because the embedded hologram's reconstructed image is darker than the reconstructed host image.
In this study, we use deep neural networks to restore the darkened image.
arXiv Detail & Related papers (2021-12-20T01:21:28Z) - Learned holographic light transport [2.642698101441705]
Holography algorithms often fall short in matching simulations with results from a physical holographic display.
Our work addresses this mismatch by learning the holographic light transport in holographic displays.
Our method can dramatically improve simulation accuracy and image quality in holographic displays.
arXiv Detail & Related papers (2021-08-01T12:05:33Z) - Learned Spatial Representations for Few-shot Talking-Head Synthesis [68.3787368024951]
We propose a novel approach for few-shot talking-head synthesis.
We show that this disentangled representation leads to a significant improvement over previous methods.
arXiv Detail & Related papers (2021-04-29T17:59:42Z) - Deep DIH : Statistically Inferred Reconstruction of Digital In-Line
Holography by Deep Learning [1.4619386068190985]
Digital in-line holography is commonly used to reconstruct 3D images from 2D holograms for microscopic objects.
In this paper, we propose a novel implementation of autoencoder-based deep learning architecture for single-shot hologram reconstruction.
arXiv Detail & Related papers (2020-04-25T20:39:25Z) - State of the Art on Neural Rendering [141.22760314536438]
We focus on approaches that combine classic computer graphics techniques with deep generative models to obtain controllable and photo-realistic outputs.
This report is focused on the many important use cases for the described algorithms such as novel view synthesis, semantic photo manipulation, facial and body reenactment, relighting, free-viewpoint video, and the creation of photo-realistic avatars for virtual and augmented reality telepresence.
arXiv Detail & Related papers (2020-04-08T04:36:31Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.