Compressive Optical Imaging: Architectures and Algorithms
[DOI] [PDF]
Abstract
Many traditional optical sensors are designed to collect directly interpretable and intuitive measurements. For instance, a standard digital camera directly measures the intensity of a scene at different spatial locations to form a pixel array. Recent advances in the fields of image reconstruction, inverse problems, and compressive sensing (CS) indicate, however, that substantial performance gains may be possible in many contexts via less direct measurements combined with computational methods. In particular, CS allows for the extraction of high-resolution images from relatively small focal plane arrays (FPAs). The basic idea of CS theory is that when the image of interest is very sparse or highly compressible in some basis (i.e., most basis coefficients are small or zero-valued), relatively few well- chosen observations suffice to reconstruct the most significant non-zero components. In particular, judicious selection of the type of image transformation introduced by measurement systems may dramatically improve our ability to extract high-quality images from a limited number of measurements. By designing optical sensors to collect measurements of a scene according to CS theory, we can use sophisticated computational methods to infer critical scene structure and content.
Citation
R. F. Marcia, R. M. Willett and drz.ac, “Compressive optical imaging: Architectures and algorithms”, in Optical and Digital Image Processing: Fundamentals and Applications, G. Cristobal, P. Schelkens and H. Thienpont, Eds. Wiley-VCH Verlag GmbH & Co, 2011, 485–505.
BibTeX
@incollection{marcia-odip2011-compressiveopticalimaging,
doi = {10.1002/9783527635245.ch22}, title = {Compressive optical imaging: Architectures and algorithms}, author = {Marcia, Roummel F. and Willett, Rebecca M. and Harmany, Zachary T.}, booktitle = {Optical and Digital Image Processing: Fundamentals and Applications}, editor = {Cristobal, Gabriel and Schelkens, Peter and Thienpont, Hugo}, publisher = {Wiley-VCH Verlag GmbH & Co}, month = {apr}, year = {2011}, pages = {485–505}, abstract = {Many traditional optical sensors are designed to collect directly interpretable and intuitive measurements. For instance, a standard digital camera directly measures the intensity of a scene at different spatial locations to form a pixel array. Recent advances in the fields of image reconstruction, inverse problems, and compressive sensing (CS) indicate, however, that substantial performance gains may be possible in many contexts via less direct measurements combined with computational methods. In particular, CS allows for the extraction of high-resolution images from relatively small focal plane arrays (FPAs). The basic idea of CS theory is that when the image of interest is very sparse or highly compressible in some basis (i.e., most basis coefficients are small or zero-valued), relatively few well- chosen observations suffice to reconstruct the most significant non-zero components. In particular, judicious selection of the type of image transformation introduced by measurement systems may dramatically improve our ability to extract high-quality images from a limited number of measurements. By designing optical sensors to collect measurements of a scene according to CS theory, we can use sophisticated computational methods to infer critical scene structure and content.} }
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