Non-uniform Compressive Sensing Imaging Based on Image Saliency

For more effective image sampling, compressive sensing (CS) imaging methods based on image saliency have been proposed in recent years. Those methods assign higher measurement rates to salient regions, but lower measurement rate to non-salient regions to improve the performance of CS imaging. However, those methods are block-based, which are difficult to apply to actual CS sampling, as each photodiode should strictly correspond to a block of the scene. In our work, we propose a non-uniform CS imaging method based on image saliency, which assigns higher measurement density to salient regions and lower density to non-salient regions, where measurement density is the number of pixels measured in a unit size. As the dimension of the signal is reduced, the quality of reconstructed image will be improved theoretically, which is confirmed by our experiments. Since the scene is sampled as a whole, our method can be easily applied to actual CS sampling. To verify the feasibility of our approach, we design and implement a hardware sampling system, which can apply our non-uniform sampling method to obtain measurements and re-construct the images. To our best knowledge, this is the first CS hardware sampling system based on image saliency.