Joint optimization for compressive video sensing
and reconstruction under hardware constraints
Abstract. Compressive video sensing is the process of encoding multiple sub-frames into a single frame with controlled sensor exposures and
reconstructing the sub-frames from the single compressed frame. It is
known that spatially and temporally random exposures provide the most
balanced compression in terms of signal recovery. However, sensors that
achieve a fully random exposure on each pixel cannot be easily realized
in practice because the circuit of the sensor becomes complicated and incompatible with the sensitivity and resolution. Therefore, it is necessary
to design an exposure pattern by considering the constraints enforced
by hardware. In this paper, we propose a method of jointly optimizing the exposure patterns of compressive sensing and the reconstruction
framework under hardware constraints. By conducting a simulation and
actual experiments, we demonstrated that the proposed framework can
reconstruct multiple sub-frame images with higher quality.