Abstract
n this paper we present a new image sensor architecture for fast and accurate compressive sensing (CS) of natural images. Measurement matrices usually employed in compressive sensing CMOS image sensors (CS-CIS) are recursive pseudo-random binary matrices. We have proved that the restricted isometry property (RIP) of these matrices is limited by a low sparsity constant. The quality of these matrices is also affected by the non-idealities of pseudo-random numbers generators (PRNG). To overcome these limitations, we propose a hardware-friendly pseudo-random ternary measurement matrix generated on-chip by means of class III elementary cellular automata (ECA). These ECA present a chaotic behaviour that emulates random CS measurement matrices better than other PRNG. We have combined this new architecture with a block-based CS smoothed-projected Landweber (BCS-SPL) reconstruction algorithm. By means of single value decomposition (SVD) we have adapted this algorithm to perform fast and precise reconstruction while operating with binary and ternary matrices. Simulations are provided to qualify the approach