Coded Aperture Lensless Imager (CALI)
RIPS 2009 Program Plan: Areté Associates
1. Background
The vast majority of visible band sensors employed for modern intelligence, surveillance
and reconnaissance (ISR) applications use conventional, lensed camera systems. These
have sufficient performance for a wide variety of applications, but can be limited for
those that require low mass, low volume, extensive depth of field or wide field viewing
with minimal aberration.
The most basic alternative to conventional camera systems is the familiar pinhole camera
(Figure 1). Notably, these lensless systems are simple, lightweight and have infinite
depth of field. However, photon flux is limited by small aperture size; as such, required
exposure times are high and applications are limited to stationary or slowly varying
scenes. Additional light can be captured with a larger pinhole, but spatial resolution
suffers.
Figure 1. Schematic illustration of pinhole camera imaging
[http://commons.wikimedia.org/wiki/Image:Pinhole-camera.png].
Instead, we consider a lensless imaging system with a multitude of subapertures. Photon
flux goes up, required exposure time goes down, but the multiplexed image recorded by
the detector must be reconstructed using correlation or deconvolution methods to obtain a
realistic representation of the original scene (Figure 2).
Figure 2. Schematic illustration of coded aperture imaging
[Fenimore & Cannon, 1978].
This reconstruction is often ill-posed, but subaperture arrays that maximize light
transmission and minimize the amplitude of autocorrelation sidelobes can be chosen to
provide relatively high signal-to-noise properties and optimal reconstruction [e.g.,
Fenimore & Cannon, 1978]. Such lensless, coded aperture arrays have been successfully
employed in systems for γ- and X-ray imaging where conventional imaging approaches
are not possible (e.g. the European Space Agency’s Integral satellite gamma ray camera).
Recently, coded aperture imaging in the visible and infrared bands has been demonstrated
and investigated for application to wide field surveillance by way of DARPA’s Large
Area Coverage Optical Search While Track and Engage (LACOSTE) program, little data
is available regarding design trades and system performance [e.g. Slinger et al., 2007;
Ridley et al, 2009].
2. Problem statement
Perform computer simulations to assess optical performance of visible band coded
aperture lensless camera systems and develop algorithms to recover scene content from
and improve image quality of simulated and laboratory camera data.
Simulations should assess spatial resolution and field of view as a function of
pinhole/subaperture dimensions and array geometry. Emphasis here should be placed on
evaluating feasibility of using coded aperture arrays for visible light imaging and
demonstrating differences between various optical designs. As such, we recommend
employing available open source optical propagation code such as PROPER [Krist, 2007]
for simulations.
Algorithm development should entail review, implementation and evaluation of state-ofthe-art methods available in the open literature for deconvolution and image postprocessing. Example processing methods include, but are not limited to, Wiener filtering,
Lucy-Richardson deconvolution and total variation methods [e.g., Richardson, 1972;
Lucy, 1974; Rudin et al., 1992; Buades et al., 2005; Chan et al., 2005; Osher et al.,
2005]. Versions of these and other more advanced approaches may be available in open
source code libraries for review, testing and evaluation. Efforts here should be directed at
identifying and implementing candidate algorithms that produce the best quality image in
terms of accuracy with respect to the original scene, maximizing signal-to-noise ratio
(SNR) and minimizing effects of diffraction and geometric blur.
A testbed camera system has been developed by Areté for the purposes of this project.
This system will be used to capture visible band image data using several pre-defined
apertures including single pinhole, random array and modified uniformly redundant array
(MURA). Additional specifications and collected data will be provided by Areté staff
during initial weeks of the program.
3. Technical tasks
1. Literature review
a. Simulation: Research fundamental optics, theoretical performance of
lensless camera systems and available optical propagation models.
b. Algorithm: Identify candidate deconvolution and image processing
algorithms.
2. Initial development
a. Simulation: Develop basic simulations of single pinhole and coded
aperture cameras; evaluate basic optical performance in terms of system
point spread function, spatial resolution and field of view.
b. Algorithm: Select promising deconvolution methods and demonstrate
image recovery from simulated data.
3. Image quality metric: Develop a metric to quantify image quality that can be used
to compare original scene content with processed data recovered from lensless
camera. The metric should evaluate spatial frequency content and signal-to-noise
ratio.
4. Validation: Given specifications and data from laboratory imaging system, tailor
simulations to mimic testbed setup and apply processing algorithms to the data
provided to validate accuracy/performance. Assess discrepancies with real world
data and identify refinements needed for improved simulation and processing.
If time allows, teams may confirm or begin implementation of refinements identified in
task 4. However, emphasis throughout should be placed on addressing key questions:
optimal pinhole/subaperture size, focal length, as well as costs and benefits of various
array morphologies and candidate deconvolution algorithms.
4. Program deliverables and notes
Program deliverables
1. Midterm briefing: provide a short briefing to Areté technical team accounting
status of research during program week 4/5 (final date and location TBD). This
briefing should also include a discussion of outstanding technical or logistical
issues and outline anticipated content of project final report.
2. Final report: summarize literature review, describe algorithms employed and
assess the merit and feasibility of coded aperture lensless imaging in visible
wavelengths. Include recommendations for future hardware and software
development. Student team should also compose and present a final briefing of
results and recommendations (see Deliverable 4).
3. Code package: include standalone simulation and processing algorithm source
code (in IDL or Matlab), documentation. An example case should be included in
the standalone package that demonstrates end-to-end simulation of
4. Site visit: present final results and hand off deliverables.
Program notes
 Kick-off meeting: Areté staff to give an overview of the problem statement and
expected program outputs. Subsequent meetings will be scheduled to go over
research details
 Weekly meetings: One or more Areté staff will meet with the student team to answer
questions, provide research guidance and review progress status. Students are
expected to give verbal presentation of work progress, discuss issues at these
meetings.
 Areté staff and UCLA faculty support: Areté anticipates an exciting, collaborative
technical program primarily directed by the RIPS student team with expert on-site
support provided by the UCLA faculty mentor and frequent interaction with the Areté
technical team via email, telephone and pre-arranged visits.
5. Selected references
Buades, A., Coll, B., and Morel, J.M., (2005), A review of image denoising algorithms, with a new
one, SIAM Multiscale Model. Simul., 4, 490.
Chan, T. et al., (2005), Recent developments in total variation image restoration, in Mathematical
Models of Computer Vision, N. Paragios, Y. Chen, and O. Faugeras (Eds), Springer Verlag, 17.
Lucy, L.B. (1974), An iterative technique for the rectification of observed distributions, Ast. J.,
79(6), 745.
Osher, S. et al. (2005), An iterative regularization method for total variation-based image
restoration, SIAM Multiscale Model. Simul., 4, 460.
Richardson, W.H. (1972), Bayesian-Based Iterative Method of Image Restoration, J. Opt. Soc.
Am., 62(1), 55.
Ridley, K.D., et al. (2009), Visible band lens-free imaging using coded aperture techniques, Proc.
SPIE, 7468, 746809.
Rudin, L.I., Osher, S. and Fatemi, E. (1992), Nonlinear total variation based noise removal
algorithms, Phys. D., 60, 259.
Slinger, C., et al. (2007), Coded aperture systems as nonconventional, lensless imagers for the
visible and infrared, Proc. SPIE, 6737, 67370D.