Satoshi YAMAMOTO^*, Yasumasa ITAKURA¹, Masashi SAWABE¹, Gimpei OKADA¹, Toshiya NAKAGUCHI², and Norimichi TSUMURA¹

Department of Japanese–Oriental (Kampo) Medicine, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8670, Japan
¹Graduate School of Advanced Integration Science, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
²Graduate School of Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan

(Received January 21, 2011; Accepted February 18, 2011)

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-single-pixel 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.

Key words: image-based relighting, compressive sensing

^*Present address: Center for Kampo Medicine, School of Medicine, Keio University, Shinjuku, Tokyo 160-8582, Japan. E-mail address: may-s@umin.net