3D compressive sensing

Xianbiao Shu let me know of his recent paper on evaluating 3D for a series of frames using compressive sensing.

Imaging via Three-dimensional Compressive Sampling (3DCS) by Xianbiao Shu, Narendra Ahuja. The abstract reads:

Compressive sampling (CS) aims at acquiring a signal at a sampling rate that is significantly below the Nyquist rate. Its main idea is that a signal can be decoded from incomplete linear measurements by seeking its sparsity in some domain. Despite the remarkable progress in the theory of CS, little headway has been made in the compressive imaging (CI) camera. In this paper, a three-dimensional compressive sampling (3DCS) approach is proposed to reduce the required sampling rate of the CI camera to a practical level. In 3DCS, a generic three-dimensional sparsity measure (3DSM) is presented, which decodes a video from incomplete samples by exploiting its 3D piecewise smoothness and temporal low-rank property. In addition, an efficient decoding algorithm is developed for this 3DSM with guaranteed convergence. The experimental results show that our 3DCS requires a much lower sampling rate than the existing CS methods without compromising recovery accuracy.

I'd have to read it but it reminds me a little about the background substraction work [1].




[1] V. Cevher, A. Sankaranarayanan, M. F. Duarte, D. Reddy, R. G. Baraniuk, and R. Chellappa. Compressive Sensing for Background Subtraction. In European Conference on Computer Vision (ECCV), 2008.

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Image above: An Atlas V rocket with NASA's Juno spacecraft at Space Launch Complex 41 of the Cape Canaveral Air Force Station in Florida. The white color on the first stage is not paint, but frost caused by the supercold liquid oxygen used by the Atlas V's first stage engine. Launch Window: 11:34 a.m. to 12:43 p.m. EDT