On adaptive pixel random selection for Compressive Sensing

Recently developed Compressive Sensing image sensor architectures tend to provide compact on-chip implementations to perform alternative acquisitions. On the other hand, the time of reconstruction generally limits possible applications taking advantage of those specific sensing schemes. This work proposes an entire Compressive Sensing system composed of an encoder (a dedicated imager top-level architecture) and a decoder (a reconstruction algorithm). The proposed system provides a compromise between the sensing scheme efficiency for relaxing on-chip constraints and the reconstruction complexity/quality. This system performs an adaptive block-based sensing, particularly well suited for video acquisition because of being combined with a fast inpainting based reconstruction algorithm. The simulation results show that compared to state of the art reconstructions and without important image degradation, the proposed reconstruction algorithm considerably reduces the computation time.