Scientific Computation is emerging as absolutely central to the scientific method. Unfortunately, it is error-prone and currently immature: traditional scientific publication is incapable of finding and rooting out errors in scientific computation; this must be recognized as a crisis. Reproducible computational research, in which the full computational environment that produces a result is published along with the article, is an important recent development, and a necessary response to this crisis. We have been practicing reproducible computational research for 15 years and integrated it with our scientific research, and with doctoral and postdoctoral education. In this article, we review our approach, how the approach has spread over time, and how science funding agencies could help spread the idea more rapidly. 1

Abstract — Compressive imaging (CI) is a natural branch of compressed sensing (CS). Although a number of CI implementations have started to appear, the design of efficient CI system still remains a challenging problem. One of the main difficulties in implementing CI is that it involves huge amounts of data, which has far-reaching implications for the complexity of the optical design, calibration, data storage and computational burden. In this paper, we solve these problems by using a twodimensional separable sensing operator. By so doing, we reduce the complexity by factor of 10 6 for megapixel images. We show that applying this method requires only a reasonable amount of additional samples.

Compressive imaging (CI) is a natural branch of compressed sensing (CS). One of the main difficulties in implementing CI is that, unlike many other CS applications, it involves huge amount of data. This data load has extensive implications for the complexity of the optical design, for the complexity of calibration, for data storage requirements. As a result, practical CI implementations are mostly limited to relative small image sizes. Recently we have shown that it is possible to overcome these problems by using a separable imaging operator. We have demonstrated that separable imaging operator permits CI of megapixel size images and we derived a theoretical bound for oversampling factor requirements. Here we further elaborate the tradeoff of using separable imaging operator, present and discuss additional experimental results.

3.4.4 Trying compressive sensing..................... 34 3.5 LP-HP compressive sensing......................... 34 3.5.1 LP-HP CS- experiments...................... 39 3.6 CS regularization............................... 41 3.6.1 CS with ℓ1-TV- optimization.................... 41 3.6.2 CS with TV- results......................... 43 3.7 LP-HP CS with TV............................. 43 3.8 CS of images- conclusion.......................... 43 3.9 Generalized OMP- GOMP (future work)................. 47 4 Sampling and noise 49 4.1 Reconstruction error estimation....................... 50 4.1.1 Signal model and more assumptions................ 50 4.1.2 Least Squares post-processing.................... 51 4.1.3 Sampling matrices, noise and reconstruction error........ 51