Fourier depth of field
Optical systems used in photography and cinema produce depth-of-field effects, that is, variations of focus with depth. These effects are simulated in image synthesis by integrating incoming radiance at each pixel over the lense aperture. Unfortunately, aperture integration is extremely costly for d...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Association for Computing Machinery (ACM)
2015
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| Online Access: | http://hdl.handle.net/1721.1/100281 https://orcid.org/0000-0001-9919-069X |
| _version_ | 1826213005764853760 |
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| author | Soler, Cyril Subr, Kartic Durand, Fredo Holzschuch, Nicolas Sillion, Francois |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Soler, Cyril Subr, Kartic Durand, Fredo Holzschuch, Nicolas Sillion, Francois |
| author_sort | Soler, Cyril |
| collection | MIT |
| description | Optical systems used in photography and cinema produce depth-of-field effects, that is, variations of focus with depth. These effects are simulated in image synthesis by integrating incoming radiance at each pixel over the lense aperture. Unfortunately, aperture integration is extremely costly for defocused areas where the incoming radiance has high variance, since many samples are then required for a noise-free Monte Carlo integration. On the other hand, using many aperture samples is wasteful in focused areas where the integrand varies little. Similarly, image sampling in defocused areas should be adapted to the very smooth appearance variations due to blurring. This article introduces an analysis of focusing and depth-of-field in the frequency domain, allowing a practical characterization of a light field's frequency content both for image and aperture sampling. Based on this analysis we propose an adaptive depth-of-field rendering algorithm which optimizes sampling in two important ways. First, image sampling is based on conservative bandwidth prediction and a splatting reconstruction technique ensures correct image reconstruction. Second, at each pixel the variance in the radiance over the aperture is estimated and used to govern sampling. This technique is easily integrated in any sampling-based renderer, and vastly improves performance. |
| first_indexed | 2024-09-23T15:41:36Z |
| format | Article |
| id | mit-1721.1/100281 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:41:36Z |
| publishDate | 2015 |
| publisher | Association for Computing Machinery (ACM) |
| record_format | dspace |
| spelling | mit-1721.1/1002812022-10-02T03:30:05Z Fourier depth of field Soler, Cyril Subr, Kartic Durand, Fredo Holzschuch, Nicolas Sillion, Francois Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Durand, Fredo Optical systems used in photography and cinema produce depth-of-field effects, that is, variations of focus with depth. These effects are simulated in image synthesis by integrating incoming radiance at each pixel over the lense aperture. Unfortunately, aperture integration is extremely costly for defocused areas where the incoming radiance has high variance, since many samples are then required for a noise-free Monte Carlo integration. On the other hand, using many aperture samples is wasteful in focused areas where the integrand varies little. Similarly, image sampling in defocused areas should be adapted to the very smooth appearance variations due to blurring. This article introduces an analysis of focusing and depth-of-field in the frequency domain, allowing a practical characterization of a light field's frequency content both for image and aperture sampling. Based on this analysis we propose an adaptive depth-of-field rendering algorithm which optimizes sampling in two important ways. First, image sampling is based on conservative bandwidth prediction and a splatting reconstruction technique ensures correct image reconstruction. Second, at each pixel the variance in the radiance over the aperture is estimated and used to govern sampling. This technique is easily integrated in any sampling-based renderer, and vastly improves performance. 2015-12-16T02:48:18Z 2015-12-16T02:48:18Z 2009-04 Article http://purl.org/eprint/type/ConferencePaper 07300301 http://hdl.handle.net/1721.1/100281 Soler, Cyril, Kartic Subr, Fredo Durand, Nicolas Holzschuch, and Francois Sillion. “Fourier Depth of Field.” ACM Transactions on Graphics 28, no. 2 (April 1, 2009): 1–12. https://orcid.org/0000-0001-9919-069X en_US http://dx.doi.org/10.1145/1516522.1516529 ACM Transactions on Graphics Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for Computing Machinery (ACM) MIT web domain |
| spellingShingle | Soler, Cyril Subr, Kartic Durand, Fredo Holzschuch, Nicolas Sillion, Francois Fourier depth of field |
| title | Fourier depth of field |
| title_full | Fourier depth of field |
| title_fullStr | Fourier depth of field |
| title_full_unstemmed | Fourier depth of field |
| title_short | Fourier depth of field |
| title_sort | fourier depth of field |
| url | http://hdl.handle.net/1721.1/100281 https://orcid.org/0000-0001-9919-069X |
| work_keys_str_mv | AT solercyril fourierdepthoffield AT subrkartic fourierdepthoffield AT durandfredo fourierdepthoffield AT holzschuchnicolas fourierdepthoffield AT sillionfrancois fourierdepthoffield |