A practical enhanced-resolution integrated optical-digital imaging camera (PERIODIC)

An integrated array computational imaging system, dubbed PERIODIC, is presented which is capable of exploiting a diverse variety of optical information including sub-pixel displacements, phase, polarization, intensity, and wavelength. Several applications of this technology will be presented including digital superresolution, enhanced dynamic range and multi-spectral imaging. Other applications include polarization based dehazing, extended depth of field and 3D imaging. The optical hardware system and software algorithms are described, and sample results are shown.

[1]  J. Tanida,et al.  Thin Observation Module by Bound Optics (TOMBO): Concept and Experimental Verification. , 2001, Applied optics.

[2]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[3]  S. Kavadias,et al.  A logarithmic response CMOS image sensor with on-chip calibration , 2000, IEEE Journal of Solid-State Circuits.

[4]  Jun Tanida,et al.  Reconstruction of a high-resolution image on a compound-eye image-capturing system. , 2004, Applied optics.

[5]  R. Barnard,et al.  High-resolution iris image reconstruction from low-resolution imagery , 2006, SPIE Optics + Photonics.

[6]  Ki-Sang Hong,et al.  Extending dynamic range of two color images under different exposures , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[7]  Tuan Vo-Dinh A hyperspectral imaging system for in vivo optical diagnostics , 2004, IEEE Engineering in Medicine and Biology Magazine.

[8]  Chein-I Chang,et al.  Unsupervised interference rejection approach to target detection and classification for hyperspectral imagery , 1998 .

[9]  David M. Haaland,et al.  Hyperspectral imaging of biological targets: the difference a high resolution spectral dimension and multivariate analysis can make , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[10]  Qian Du,et al.  Automatic target recognition for hyperspectral imagery using high-order statistics , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[11]  Hao Chen,et al.  Forest information from hyperspectral sensing , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[12]  Johannes R. Sveinsson,et al.  Classification of hyperspectral data from urban areas based on extended morphological profiles , 2005, IEEE Transactions on Geoscience and Remote Sensing.