RESUMO
Compressive imagers acquire images, or other optical scene information, by a series of spatially filtered intensity measurements, where the total number of measurements required depends on the desired image quality. Compressive imaging (CI) offers a versatile approach to optical sensing which can improve size, weight, and performance (SWaP) for multispectral imaging or feature-based optical sensing. Here we report the first (to our knowledge) systematic performance comparison of a CI system to a conventional focal plane imager for binary, grayscale, and natural light (visible color and infrared) scenes. We generate 1024×1024 images from a range of measurements (0.1%-100%) acquired using digital (Hadamard), grayscale (discrete cosine transform), and random (Noiselet) CI basis sets. Comparing the outcome of the compressive images to conventionally acquired images, each made using 1% of full sampling, we conclude that the Hadamard Transform offered the best performance and yielded images with comparable aesthetic quality and slightly higher spatial resolution than conventionally acquired images.
