Compressive Volume Rendering
| atmire.migration.oldid | 3369 | |
| dc.contributor.advisor | Alim, Usman | |
| dc.contributor.author | Liu, Xiaoyang | |
| dc.date.accessioned | 2015-07-09T22:00:53Z | |
| dc.date.available | 2015-11-20T08:00:33Z | |
| dc.date.issued | 2015-07-09 | |
| dc.date.submitted | 2015 | en |
| dc.description.abstract | Compressive rendering refers to the process of reconstructing a full image from a small subset of the rendered pixels, thereby expediting the rendering task. Images produced via direct volume rendering are usually highly compressible in a transform domain such as the Fourier or wavelet domains. In this dissertation, we empirically investigate four image order tech- niques for compressive rendering that are suitable for direct volume rendering. The first technique is based on the theory of compressed sensing and leverages the sparsity of the image gradient in the Fourier domain. Following this, we investigate sparse representation of volume rendered images via dictionary learning. The latter techniques exploit smoothness properties of the rendered image; the third technique recovers the missing pixels via a to- tal variation minimization procedure while the fourth technique incorporates a smoothness prior in a variational reconstruction framework employing interpolating cubic B-splines. We compare and contrast these four techniques in terms of quality, efficiency and sensitivity to the distribution of pixels. Our results show that smoothness-based techniques significantly outperform techniques that are based on compressed sensing as well as dictionary learning and are also robust in the presence of highly incomplete information. We achieve high quality recovery with as little as 20% of the pixels distributed uniformly in screen space. | en_US |
| dc.identifier.citation | Liu, X. (2015). Compressive Volume Rendering (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25391 | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/25391 | |
| dc.identifier.uri | http://hdl.handle.net/11023/2346 | |
| dc.language.iso | eng | |
| dc.publisher.faculty | Graduate Studies | |
| dc.publisher.institution | University of Calgary | en |
| dc.publisher.place | Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | |
| dc.subject | Computer Science | |
| dc.subject.classification | Volume rendering | en_US |
| dc.subject.classification | Image recovery | en_US |
| dc.subject.classification | Compressed sensing | en_US |
| dc.title | Compressive Volume Rendering | |
| dc.type | master thesis | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of Calgary | |
| thesis.degree.name | Master of Science (MSc) | |
| ucalgary.item.requestcopy | true |