New techniques in computational thermal imaging

In the last ten years, computational imaging has become established as a developing field of research; creating space and opportunity for emerging technologies to innovate the market from traditional imaging techniques. This is through the transfer of complexity from optics to digital signal processing, where, thanks to Moore’s law, high performance can be achieved at very low cost. These emerging technologies provide cheaper and more targeted imaging alternatives attractive to consumers and researchers alike. This poster will present the new technologies under development at Glasgow university in this contemporary field.

Wavefront coding has long offered the prospect of mitigating optical aberrations and extended depth of field [1], but image quality and noise performance are inevitably reduced [2]. We report on progress in the use of agile encoding and pipelined fusion of image sequences to recover image quality.

In this poster, we also report on multi-aperture imaging employing arrays of subsystems to achieve higher performance from simpler optics [3], requiring integrated computational image recovery from the individual sub-images. Using multi-spectral, multi-aperture imaging, we have imaged gas clouds in the thermal infrared, as well as plastics, for their detection and classification.

A super-resolution (SR) system in the long-wave infrared (LWIR) based on a synchronous array of low-cost uncooled LWIR cameras is also presented, with enhanced angular resolution and 3D integral imaging capabilities able to image through obstacles at full-frame video rate. Also presented is variations of the multi-aperture architecture, foveal imaging [4] a more targeted version of multi-aperture imaging.

The attraction of these emerging technologies is the possibility of achieving new functionalities without an increase in optical complexity, and we describe in this poster several practical multi-aperture and wavefront encoding solutions that provide benefit in the infrared.

References

[1] Muyo, G., & Harvey, A. R. (2005). Decomposition of the optical transfer function: wavefront coding imaging systems. Optics Letters, 30(20), 2715–7. https://doi.org/10.1364/OL.30.002715

[2] Vettenburg, T., Bustin, N., & Harvey, A. R. (2010). Fidelity optimization for aberration-tolerant hybrid imaging systems. Optics Express, 18(9), 9220–9228. https://doi.org/10.1364/OE.18.009220

[3] Carles, G., Muyo, G., Bustin, N., Wood, A., & Harvey, A. R. (2015). Compact multi-aperture imaging with high angular resolution. Journal of the Optical Society of America A, 32(3), 411. https://doi.org/10.1364/JOSAA.32.000411

[4] Carles, G., Chen, S., Bustin, N., Downing, J., McCall, D., Wood, A., & Harvey, A. R. (2016). Multi-aperture foveated imaging. Optics Letters, 41(8), 1869. https://doi.org/10.1364/OL.41.001869