Super-Resolution Model for a Compressed-Sensing Measurement Setup

In this paper, we present a new compressed-sensing (CS) setup together with a new scalable CS model, which allows the tradeoff between system complexity (number of detectors) and time (number of measurements). We describe the calibration of the system with respect to model parameters and show the reconstruction of compressed measurements according to the new model, which are acquired with the proposed setup. The proposed model and its parameter are evaluated with the established measures, i.e., restricted isometry property and coherence. The resulting consequences for usable sparsifying basis are derived on this evaluation. With the proposed setup, it is possible to acquire high-resolution images with a low-resolution camera.