By Adrian Stern
This devoted evaluate of optical compressive imaging addresses implementation elements of the innovative thought of compressive sensing (CS) within the box of optical imaging and sensing. It overviews the technological possibilities and demanding situations interested by optical layout and implementation, from easy thought to optical architectures and structures for compressive imaging in quite a few spectral regimes, spectral and hyperspectral imaging, polarimetric sensing, 3-dimensional imaging, super-resolution imaging, lens-free, on-chip microscopy, and part sensing and retrieval. The reader will achieve a whole creation to idea, test, and useful use for decreasing undefined, shortening snapshot scanning time, and enhancing photo solution in addition to different functionality parameters. Optics practitioners and optical process designers, electric and optical engineers, mathematicians, and sign processing pros will all locate the ebook a different trove of data and sensible guidance.
- Delivers the 1st booklet on compressed sensing facing procedure improvement for a wide selection of optical imaging and sensing applications.
- Covers the basics of CS conception, together with noise and algorithms, in addition to easy layout techniques for information acquisition in optics.
- Addresses the demanding situations of imposing compressed sensing concept within the context of alternative optical imaging designs, from 3D imaging to tomography and microscopy.
- Provides a vital source for the layout of recent and better units with more desirable photo caliber and shorter acquisition times.
Adrian Stern, PhD, is affiliate professor and head of the Electro-Optical Engineering Unit at Ben-Gurion college of the Negev, Israel. he's an elected Fellow of SPIE.
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Additional info for Optical compressive imaging
2009), Rivenson et al. (2010), and Shechtman et al. (2011). References Barrett, H. H. and K. J. Myers. 2003. Foundations of Image Science. , October 2003. Brady, D. J. 2009. Optical Imaging and Spectroscopy. John Wiley & Sons, Hoboken, NJ. Donoho, D. L. and M. Elad. 2003. Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization. Proceedings of the National Academy of Sciences of the United States of America 100(5): 2197–2202. Elad, M. 2010. Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing.
Two-Point Imaging Resolution Revisited Using CS Tools . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 20 21 22 23 23 27 Introduction Readers with classical optics education may not find the compressive sensing (CS) notions introduced in Chapter 1 so natural or corresponding well with the familiar optical science and engineering lexicon. This is partly because CS theory was born and has evolved mainly in fields such as information theory, computational mathematics, and signal processing, and, therefore, its formalism, notions, and concepts do not necessarily overlap those found in typical optics textbooks.
Restricted Isometry Property. . . . . . . . . . . . . . . . . . . Coherence . . . . . . . . . . . . . . . . . . . . . . . . . . Two-Point Imaging Resolution Revisited Using CS Tools . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 20 21 22 23 23 27 Introduction Readers with classical optics education may not find the compressive sensing (CS) notions introduced in Chapter 1 so natural or corresponding well with the familiar optical science and engineering lexicon.