Abstract

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 $1024\times 1024$ 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.

© 2013 Optical Society of America

Efficient adaptation of complex-valued noiselet sensing matrices for compressed single-pixel imaging

Anna Pastuszczak, Bartłomiej Szczygieł, Michał Mikołajczyk, and Rafał Kotyński
Appl. Opt. 55(19) 5141-5148 (2016)

Focal plane array-based compressive imaging in medium wave infrared: modeling, implementation, and challenges

Zimu Wu and Xia Wang
Appl. Opt. 58(31) 8433-8441 (2019)

Compressive ultraspectral imaging using multiscale structured illumination

Vladislav Kravets, Paul Kondrashov, and Adrian Stern
Appl. Opt. 58(22) F32-F39 (2019)

Phase and amplitude reconstruction in single-pixel transmission microscopy: a comparison of Hadamard, cosine, and noiselet bases

A. Santos-Amador, M. Araiza-Esquivel, H. González, A. Rodríguez-Cobos, E. Tajahuerce, L. Martínez-León, G. Ramírez-Flores, and R. E. Balderas-Navarro
Appl. Opt. 60(23) 6935-6942 (2021)

Compressive spectral imaging approach using adaptive coded apertures

Hao Zhang, Xu Ma, and Gonzalo R. Arce
Appl. Opt. 59(7) 1924-1938 (2020)