The k-wave toolbox is used to construct a compressed-sensing-based photoacoustic imaging model. And the image is reconstructed by the Jacques Hadamard Observation Matrix and the two reconstruction algorithms (OMP and ROMP). The restored image contains the main information of the original image from the visual and PSNR value, it means that the original image can be restored by the combination of Compressed Sensing theory and suitable de-noising method. Compared with Nyquist's sampling method, the amount of data collected by our compressed sensing theory is greatly reduced, which saves resources and space to a great extent. This theory has a great advantage for the photoacoustic imaging of big data, and also can provide the convenience of time for the following image analysis. By the way, it's been experimentally determined that the ROMP reconstruction algorithm has a better reconstruction effect than OMP reconstruction algorithm does.
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