Monte–Carlo Techniques Applied to CGH Generation Processes and Their Impact on the Image Quality Obtained
ABSTRACT Computer graphics aim to create visual representations for screens, where depth is simulated. In contrast, Computed Generated Holograms (CGH) focus on encoding and recreating light patterns to generate a true 3D holographic image that appears as a physical object in space. Therefore, althou...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
|
| Series: | Engineering Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/eng2.13109 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832576599055138816 |
|---|---|
| author | Juan A. Magallón Alfonso Blesa Francisco J. Serón |
| author_facet | Juan A. Magallón Alfonso Blesa Francisco J. Serón |
| author_sort | Juan A. Magallón |
| collection | DOAJ |
| description | ABSTRACT Computer graphics aim to create visual representations for screens, where depth is simulated. In contrast, Computed Generated Holograms (CGH) focus on encoding and recreating light patterns to generate a true 3D holographic image that appears as a physical object in space. Therefore, although both use digital models, the computation of CGHs necessitates additional phase‐related calculations, which in turn escalate computational demands. These calculations often result in excessively long development times or, at worst, render the process unfeasible. In order to reduce computational time, Partial Monte–Carlo Sampling (PMCS) techniques for CGH generation are presented, integrating them into the whole process of generating a CGH for a synthetic 3D scene, from design to rendering. PMCS is based on the random choice of a subset of rays used to compute the CGH and relates the computation time spent to the quality of the reconstructed scene. Quantitative analysis shows that PMCS does not significantly compromise image quality. Both simulated and in‐laboratory image reconstruction from holograms demonstrates consistent trends, showcasing improved quality with higher numbers of rays and increased resolution. Furthermore, we establish a direct relationship between image quality and computational time, which effectively addresses specific requirements. |
| format | Article |
| id | doaj-art-1aa89e94e4ea4643b97a7f77779ecf23 |
| institution | Kabale University |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-1aa89e94e4ea4643b97a7f77779ecf232025-01-31T00:22:49ZengWileyEngineering Reports2577-81962025-01-0171n/an/a10.1002/eng2.13109Monte–Carlo Techniques Applied to CGH Generation Processes and Their Impact on the Image Quality ObtainedJuan A. Magallón0Alfonso Blesa1Francisco J. Serón2Department of Computer Sciences Universidad de Zaragoza Zaragoza SpainDepartment of Electronics Engineering Universidad de Zaragoza Teruel SpainDepartment of Computer Sciences Universidad de Zaragoza Zaragoza SpainABSTRACT Computer graphics aim to create visual representations for screens, where depth is simulated. In contrast, Computed Generated Holograms (CGH) focus on encoding and recreating light patterns to generate a true 3D holographic image that appears as a physical object in space. Therefore, although both use digital models, the computation of CGHs necessitates additional phase‐related calculations, which in turn escalate computational demands. These calculations often result in excessively long development times or, at worst, render the process unfeasible. In order to reduce computational time, Partial Monte–Carlo Sampling (PMCS) techniques for CGH generation are presented, integrating them into the whole process of generating a CGH for a synthetic 3D scene, from design to rendering. PMCS is based on the random choice of a subset of rays used to compute the CGH and relates the computation time spent to the quality of the reconstructed scene. Quantitative analysis shows that PMCS does not significantly compromise image quality. Both simulated and in‐laboratory image reconstruction from holograms demonstrates consistent trends, showcasing improved quality with higher numbers of rays and increased resolution. Furthermore, we establish a direct relationship between image quality and computational time, which effectively addresses specific requirements.https://doi.org/10.1002/eng2.13109Computer Generated HologramMonte–Carloray tracing |
| spellingShingle | Juan A. Magallón Alfonso Blesa Francisco J. Serón Monte–Carlo Techniques Applied to CGH Generation Processes and Their Impact on the Image Quality Obtained Engineering Reports Computer Generated Hologram Monte–Carlo ray tracing |
| title | Monte–Carlo Techniques Applied to CGH Generation Processes and Their Impact on the Image Quality Obtained |
| title_full | Monte–Carlo Techniques Applied to CGH Generation Processes and Their Impact on the Image Quality Obtained |
| title_fullStr | Monte–Carlo Techniques Applied to CGH Generation Processes and Their Impact on the Image Quality Obtained |
| title_full_unstemmed | Monte–Carlo Techniques Applied to CGH Generation Processes and Their Impact on the Image Quality Obtained |
| title_short | Monte–Carlo Techniques Applied to CGH Generation Processes and Their Impact on the Image Quality Obtained |
| title_sort | monte carlo techniques applied to cgh generation processes and their impact on the image quality obtained |
| topic | Computer Generated Hologram Monte–Carlo ray tracing |
| url | https://doi.org/10.1002/eng2.13109 |
| work_keys_str_mv | AT juanamagallon montecarlotechniquesappliedtocghgenerationprocessesandtheirimpactontheimagequalityobtained AT alfonsoblesa montecarlotechniquesappliedtocghgenerationprocessesandtheirimpactontheimagequalityobtained AT franciscojseron montecarlotechniquesappliedtocghgenerationprocessesandtheirimpactontheimagequalityobtained |