Satoshi Yamamoto, Yasumasa Itakura, Masashi Sawabe, Gimpei Okada, Toshiya
Nakaguchi, and Norimichi Tsumura
Chiba University, ,Japan
International Journal of Computer Assisted Radiology and Surgery 2011 Mar;
6(2): 209-215.
Abstract
In this article, we propose an efficient and accurate compressive-sensing-based
method for estimating the light transport characteristics of real-world
scenes. Although compressive sensing allows the efficient estimation of
a high-dimensional signal with a sparse or near-to-sparse representation
from a small number of samples, the computational cost of the compressive
sensing in estimating the light transport characteristics is relatively
high. Moreover, these methods require a relatively smaller number of images
than other techniques although they still need 500?1000 images to estimate
an accurate light transport matrix. Precomputed compressive sensing improves
the performance of the compressive sensing by providing an appropriate
initial state. This improvement is achieved in two steps: 1) pseudo-singlepixel
projection by multiline projection and 2) regularized orthogonal matching
pursuit (ROMP) with initial signal. With these two steps, we can estimate
the light transport characteristics more accurately, much faster, and with
a lesser number of images
[PDF] (to appeare)