The field of computational imaging explores a new imaging system design approach by jointly designing the optical hardware and computational algorithms. This approach allows one to optically encode the rich information in the optical field for optimal detection and information extraction by post-measurement algorithms. The computational imaging approach has been widely applied to a broad range of sensing and imaging tasks by using various encoding strategies, such as depth estimation and 3D representation, multi- and hyperspectral imaging, polarimetric analysis, and object detection and tracking. Guided by this overall philosophy, computational imagers trade between computing resources and hardware complexities to acquire multiplexed measurement, and then process and reconstruct multidimensional information about a scene more efficiently than conventional imaging systems. The collection of thirteen papers in this section highlights some of the state-of-the-art research in the field, and provides an overview of computational imaging architectures and algorithms for a variety of tasks and applications.

Two papers describe computational imaging techniques that process depth information for the purposes of imaging and display. In fact, the essence of computational imaging is exemplified by Salahieh et al., where a simple optics with fixed focus causes the image of objects at different depths to be blurred, but the knowledge of the point spread function (PSF) and its depth-variant nature is used along with a depth map to computationally correct this effect, resulting in an image that is focused at all depths. Hong et al. discuss using commercially available gaming devices for sensing depth information along with color imagery, and then displaying the resulting 3D information using integral imaging displays. Three papers deal with applications of computed tomography (which is a form of 3D computational imaging) for analyzing properties of materials and gases. Sun et al. discuss the role of regularization parameters in the reconstruction of 3D volumes in diffuse optical tomography. Zhang et al. compare microlaser and microultrasonic excitation thermography to x-ray tomography for the purpose of studying flaws in polymer composites. Wang et al. discuss the use of flame chemiluminescence tomography (FCT) for instantaneous 3D diagnostics for analyzing flames with high spatial and temporal resolutions.

The application of computational imaging for analyzing spectral and polarization information is discussed in three papers. Miyazaki et al. describe a method for characterizing surface normal of black specular objects through polarization analysis of reflected light. Mejia and Arguello introduce a filtered gradient method for compressive spectral imaging that converges faster and produces better results than other existing iterative techniques. Correa et al. describe a multiple snapshot colored compressive spectral imager that improves on the performance of single snapshot spectral imager by rotating a dispersive element, so the dispersed spatio-spectral source is coded and integrated at different detector pixels in each rotation.

Four papers discuss how information sensing and task performance are integrally related in the framework of computational imaging. Masoudi et al. discuss methods for improving the shape-threat detection performance of x-ray computed tomography by optimizing adaptive measurements with single and multiple sources. Shilling and Muise discuss methods for target detection using optimum multiplexed measurements that do not require image reconstruction as an intermediate step. Gedalin et al. assess a compressive spectral imager for target detection, and report that this approach requires an order of magnitude less data than a conventional spectral imaging system, and is also considerably faster. DuBosq and Preece assess the use of a compressive imager by human operators for recognizing handheld objects. This paper is perhaps one of the first to characterize and model the performance of the compressive imager from a human perception standpoint.

Last but not least, the final paper by Shah et al. brings together different elements of computational imaging to describe an end-to-end multiplexed system for imaging and target detection. They describe the encoding strategy, the opto-electronic design, and some strategies for processing the information for either forming an image or for detecting moving objects. This paper exemplifies the potential benefits of computational imaging from an overall sensor system standpoint.

We hope that this special section provides readers with a broad overview of the many possible applications of computational imaging, and the current research thrusts in the field. We would also like to take this opportunity to thank the contributing authors and the staff of Optical Engineering in helping to compile this special section on computational imaging.