I got interested in reading this paper  because of the title, alas, the authors keep using that word but I do not think it means what they think it means.Here is the interesting bit [italic is my emphasis]
Recent work has investigated the concept of compressive sensing for imaging applications. Compressive sensing is similar in concept to image compression except that it reduces the number of samples acquired rather than remove redundancy from the image post-acquisition. In an example of this Robucci et al describe an imaging system that uses a custom sensor to perform ‘noiselet’ transforms (a form of pseudo-random wavelet transform) during image acquisition . This is an interesting approach, but it requires specialist sensor hardware that is not yet commercially available and primarily concentrates on image, rather than video, acquisition.
In this paper, we present a camera system that attempts to overcome the limitations of these strategies by utilizing a form of compressed imaging. The camera system adaptively concentrates most of the available bandwidth on a subset of pixels while sampling the remainder of the pixels at low-speed. In this way the camera is able to use most of its available bandwidth to capture images of the ROI at a relatively high-speed while simultaneously acquiring high-resolution images at low-speed using the rest of the bandwidth. In addition, the camera is able to use this full-frame information to calculate the positions of targets and update the high-speed ROIs without interrupting acquisition. This allows the camera to track and image moving targets at high-speed while simultaneously imaging the whole frame at a much lower frame rate. While the overall bandwidth of the camera is low it is utilised efficiently and this allows changing ROIs to be acquired continuously at high speed. Because the total volume of data is reduced considerably by only collecting the necessary information at the source, pressure is reduced on down-stream systems; including real-time data analysis.
Let me first emphasize something, this implementation of a camera is great but there is absolutely no need to capture the wording compressive sampling in it when the work stands on its own. The initial and most important aspect of compressive sensing is that the whole field of the image is sensed all the time. Let us be more specific:
Tracking high-speed ROI with low-speed full-frame.Finally, after simultaneously acquiring the ROI at high speed and the full-frame at a lower speed, the full-frame image can be analyzed to determine the most appropriate ROI (Figure 9). The Analysis loop can then pass any changes to the ROI addresses to the Acquisition loop. The Acquisition loop gets the most recent set of ROI addresses from the FIFO buffer when it is ready and so continuous acquisition is not interrupted. In this way moving targets can be tracked.
Here the ROI updating goes as slow as the low-speed full frame acquisition. This is where the process described in this paper cannot be described as compressive sensing. In CS, the ROI (in the case of adaptive compressive sensing) is updated at the full speed of the acquisition. So let me restate, this is a great implementation but this is not a compressive sensing implementation.
 A Reconfigurable Real-Time Compressive-Sampling Camera for Biological Applications, Bo Fu, Mark C. Pitter, Noah A. Russell.
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