Emerging theories and technologies on computational imaging

Abstract

Computational imaging describes the whole imaging process from the perspective of light transport and information transmission, features traditional optical computing capabilities, and assists in breaking through the limitations of visual information recording. Progress in computational imaging promotes the development of diverse basic and applied disciplines. In this review, we provide an overview of the fundamental principles and methods in computational imaging, the history of this field, and the important roles that it plays in the development of science. We review the most recent and promising advances in computational imaging, from the perspective of different dimensions of visual signals, including spatial dimension, temporal dimension, angular dimension, spectral dimension, and phase. We also discuss some topics worth studying for future developments in computational imaging.

Punching holes in light: recent progress in single-shot coded-aperture optical imaging

“…The detection of time-of-arrival used to be seldom performed due to the limited detection bandwidth of sensors. In recent years, however, significant advances in ultrafast laser systems [159], ultrafast detectors [160][161][162][163][164], and new computational frameworks in imaging science [165,166] have been applied in conjunction with coded apertures to enable observing transient phenomena with imaging speeds at the trillion-frame-per-second level in real time [167]. Among active-encoding approaches, a pinhole array is used with a lens to control both the intensity and the incident angles of pulses that probe a transient scene.…”

Computational optical imaging: challenges, opportunities, new trends, and emerging applications

“…The IEEE even launched a journal, IEEE Transactions on Computational Imaging, in 2015 dedicated to the topic. Qionghai Dai investigated the most recent and most promising progress in computational imaging, considering the various dimensions of visual signals including spatial, temporal, angular, spectral, and phase dimensions (Hu et al, 2017). Ravindra A. Athale discusses the progress made in Computational Imaging since the mid-1990s and identified three motivations for using Computational Imaging: when a direct measurement of the desired parameter is physically impossible, when the dimensionality of the desired parameter is incompatible with present technology, and when making an indirect measurement is more advantageous than making a direct one (Mait et al, 2012).…”