Pushing the limits of imaging using patterned illumination


Since the invention of the microscope and the telescope, engineers and scientists alike, have embarked on a perpetual quest to improve the resolving power of imaging instruments.  Ernst Abbe was the first to recognize that the resolving power of an imaging instrument is fundamentally limited by diffraction.  Following Abbe's seminal work in 1873, the scientific community has made numerous attempts to circumvent the diffraction limit.  The most important breakthroughs came in the form of Aperture Synthesis and Super-Resolution Microscopy, which revolutionized imaging at the astronomical and microscopic scales.  Their contributions to imaging have been duly recognized with Nobel prizes.

Despite breakthroughs, there exists a continuum of image scales between the microscopic and the astronomical, where-in diffraction limits the resolving power of imaging instruments.  Super-Resolved imaging at these scales is of significant military interest as it increases the tactical advantage of armed forces immersed in hostile environments.

State-of-the-art in Super-Resolution at macroscopic scales

With the aid of DoD funding, my group at SMU has developed  computational imagers whose resolving power is fully decoupled from the constraints imposed by the collection optics (such as diffraction and aberrations) and the detector (varying degrees of aliasing ranging from single photodiode to focal plane array).  Additionally, these imagers feature support for point spread function engineering, foveated imaging and ranging.  Such disparate capabilities are realized by processing images acquired under spatially structured illumination.  The Moiré fringes arising from the heterodyning of object detail and the structured illumination encapsulate spatial frequencies lost to diffraction. The deformations in the phase of the detected illumination pattern, encode range information.


Figure 1 Select experimental results that highlight the capabilities of SMU’s active computational imager. Please note that in each example the super-resolved image contains spatial detail past the diffraction limit of the collection optics.



Key takeaways

Adaptive computational imager: the next innovation in EO/IR imaging

The imagers discussed in the previous section are precursors to a fundamentally new class of imagers dubbed "adaptive computational imagers", whose functionality may be quickly adapted to suit evolving tactical needs.  Among other things, it is expected that these imagers will support target/threat detection and identification at increased standoff, in a wide field of regard, with the highest clarity, through the cover of darkness and obscurants.  The following innovations in optical imaging have precipitated the development of adaptive computational imagers

1.     Emergence of computational imagers that afford novel imaging capabilities by manipulating the light distribution in the object and image volumes.  Examples of novel capabilities includes simultaneous super-resolved imaging and ranging.

2.    Development of engineered optical beams that can squeeze light into regions smaller than the diffraction limit, albeit at the expense of side-lobes.

Additional material

·       Technical Brief [Download PDF]

·       Super-Resolution Landscape [Download PDF]

·       Slide deck [Download PDF]

·       Supplementary slides [Download PDF]

·       Poster [Download PDF]

Select publications

·       Pushing the limits of imaging using patterned illumination, PhD Dissertation, SOUTHERN METHODIST UNIVERSITY, 2014. [Link]

·       Optical super resolution using a lattice of light spots, Proc. of Computational Optical Sensing & Imaging, 2014. [Link]

·       Parsimony in PSF engineering using patterned illumination, Proc. of Computational Optical Sensing & Imaging, 2013. [Link]

·       Pushing the limits of digital imaging using structured illumination, Proc. of the International Conference on Computer Vision, Nov 2011. [Link]

·       Perspective imaging under structured light, Proc. of the European Conference on Computer Vision, 405-419, Sep 2010. [Link]


Frequently Asked Questions

[coming soon]


·       Dr. Marc Christensen (SMU)

·       Dr. Predrag Milojkovic (DARPA DSO…previously at ARL)

·       Indranil Sinharoy (SMU)

·       Dr. Vikrant Bhakta (Texas Instruments….previously at SMU)

·       Dr. Panos Papamichalis (SMU)


We would also to acknowledge the financial support provided by the U.S Army Research Laboratory under Cooperative Agreement Number W911NF-06-2-0035.


The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government.