Super-resolution reconstruction in a computational compound-eye imaging system
- 339 Downloads
From consumer electronics to biomedical applications, device miniaturization has shown to be highly desirable. This often includes reducing the size of some optical systems. However, diffraction effects impose a constraint on image quality when we simply scale down the imaging parameters. Over the past few years, compound-eye imaging system has emerged as a promising architecture in the development of compact visual systems. Because multiple low-resolution (LR) sub-images are captured, post-processing algorithms for the reconstruction of a high-resolution (HR) final image from the LR images play a critical role in affecting the image quality. In this paper, we describe and investigate the performance of a compound-eye system recently reported in the literature. We discuss both the physical construction and the mathematical model of the imaging components, followed by an application of our super-resolution algorithm in reconstructing the image. We then explore several variations of the imaging system, such as the incorporation of a phase mask in extending the depth of field, which are not possible with a traditional camera. Simulations with a versatile virtual camera system that we have built verify the feasibility of these additions, and we also report the tolerance of the compound-eye system to variations in physical parameters, such as optical aberrations, that are inevitable in actual systems.
KeywordsSuper-resolution Compound-eye Phase-mask
Unable to display preview. Download preview PDF.
- Castleman K.R. (1996). Digital image processing. Prentice Hall, Englewood Cliffs, NJGoogle Scholar
- Dowski E.R. Jr., Cathey W.T. (1995). Extended depth of field through wave-front coding. Applied Optics 34(11): 1859–1866Google Scholar
- Duparré, J., Schreiber, P., Dannberg, P., Scharf, T., Pelli, P., Völkel, R., Herzig, H.-P., & Bräuer, A. (2004). Artificial compound eyes—different concepts and their application to ultra flat image acquisition sensors. In: MOEMS and miniaturized systems IV, ser. proceedings of the SPIE, San Jose, California, USA, Vol. 5346, pp. 89–100.Google Scholar
- Goodman J.W. (1996). Introduction to fourier optics. 2nd ed. McGraw-Hill, New YorkGoogle Scholar
- Hornsey, R., Thomas, P., Wong, W., Pepic, S., Yip, K., & Krishnasamy, R. (2004). Electronic compound-eye image sensor: Construction and calibration. In: Proceedings of the IS&T/SPIE symposium on electronic imaging 2004. Niagara Falls, Ontario, Candada.Google Scholar
- Katsaggelos A.K. (1991). Digital image restoration. Springer, New YorkGoogle Scholar
- Kitamura Y., Showgenji R., Yamada K., Miyatake S., Miyamoto M. Morimoto, T., Masaki, Y., Kondou, N., Miyazaki, D., & Tanida, J. (2004). Reconstruction of a high-resoloution image on a compound-eye image-capturing system. Applied Optics, 43(8), 1719–1727.Google Scholar
- Krishnasamy, R., Wong, W., Shen, E., Pepic, S., Hornsey, R., & Thomas, P. (2004). High precision target tracking with a compound-eye image sensor. In: Canadian conference on electrical and computer engineering 2004, San Jose, California, USA.Google Scholar
- Lam E.Y. (2002). Digital restoration of defocused images in the wavelet domain. Applied Optics 41(23): 4806–4811Google Scholar
- Neumann, J., Fermüller, C., Aloimonos, Y., & Brajovic, V. (2004). Compound eye sensor for 3D ego motion estimation. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS ’04), IEEE.Google Scholar
- Tanida J., Kumagai T., Yamada K., Miyatake S., Ishida K., Morimoto T., Kondou N., Miyazaki D., Ichioka Y. (2001). Thin observation module by bound optics (TOMBO): Concept and experimental verification. Applied Optics 40(11): 1806–1813Google Scholar