The Charlotte Research Institute

The Center for Optoelectronics and Optical Communications

Grigg Hall Applied Optics and Physics Facility

Overview of Workshop

Recent advances in technologies for optical wavefront manipulation, optical detection, and digital post-processing have opened new possibilities for imaging systems in the visible and IR regimes.  This opens up the possibility of  developing imaging systems which differ in form factor and capabilities from traditional imaging systems and camera designs. The DARPA MONTAGE program pushed toward revolutionary imaging systems obtained by integration of the advancing capabilities of the individual optical, detection, and processing subsystems. This lead to an emerging capability for co-design and joint optimization of the optical, detection, and processing aspects of imaging systems.  A parallel effort, IARPA’s PERIODIC program sought new functionality by making a single camera with a large number of lenslets capturing different information about the scene.  This too lead to new ideas in what an imaging system is capable of and is pushing the integration of new microoptics technologies with new algorithms that compress data while extracting information of value.  This workshop will bring together both hardware and software engineers as well as mathematicians and physicists who are actively working on the fundamental issues of information theory and light-matter interactions, to bring new ideas to the effort.  Quite diverse communities that have not interacted before, such as the IEEE computational photography group and experts in fundamental limits to sub-wavelength optical superresolution will participate.

The main focus of MONTAGE was a next generation of ultra-small infrared cameras.  For example, the Duke University team which will be represented at the workshop have demonstrated palm sized, ultra thin cameras that uses a grid of nine lenslets to capture nine different low resolution photographs. The lenses are 4 times thinner and 50 times smaller in mass than a conventional night vision camera. The images can also be encoded through the use of pupil plane or focal plane masks to further extract information of relevance from the data set.  Software merges this information into a single digital super-resolution image of the scene. The software employs algorithms to correct for sensor distortion, noise and data losses that cause grainy, blurred images.  An example is shown alongside. This research is a significant demonstration of the potential for advanced processing algorithms in compressed imaging applications. Continuing development is focusing on further reductions in size while functionality increases.

The basic idea of compressive imaging is that one can used advanced sampling and image interpolation algorithms to produce more image pixels than one measures. This concept is important for spectral imaging systems which could include 100 spectral channels per spatial pixel. Spectral imaging enables optical systems to identify molecular components in images for biomedical and security applications.

The workshop will consider the use of multi-aperture imaging for extended depth of field and increased FOV (field of view).  New concepts employing lenslets on curved surfaces, mimicking some insect vision systems, are also being considered.  Wavefront encoding and compressive imaging is important because of the flexibility in hardware design and new imaging modalities that result. Spectral estimation and superresolution algorithms are examples of a numerical technique involving prior knowledge and an optical procedure employing filters, that can be fused together in a number of different ways.  Spectral imaging is a technique that generates a map of wavelength content per pixel, making it a useful tool in many applications including environmental remote sensing, military target discrimination, astrophysics and biomedical optics.