Fig. 1

Value of information in sensing a high-resolution image of size 1904×3000. Here, compressive linear measurements correspond to extracting the so-called features in compressive imaging [1–3]. In this example, the compressive imaging system captures five low-resolution images of size 238×275 using masks designed by Info-Greedy Sensing or random sensing (this corresponds to compressing the data into 8.32% of its original dimensionality). Info-Greedy Sensing performs much better than random features and preserves richer details in the recovered image. Details are explained in Section 4.3.2