Journal Article10.1364/AO.42.003379
Feature-specific imaging
TL;DR: This work examines feature-specific imaging using Wavelet, Karhunen-Loeve, Hadamard, and independent-component features, quantifying feature fidelity in Gaussian-, shot-, and quantization-noise environments, and demonstrates that within a high- noise environment it is possible to improve image fidelity via direct feature measurement.
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Abstract: We analyze the performance of feature-specific imaging systems. We study incoherent optical systems that directly measure linear projects of the optical irradiance distribution. Direct feature measurement exploits the multiplex advantage, and for small numbers of projections can provide higher feature-fidelity than those systems that postprocess a conventional image. We examine feature-specific imaging using Wavelet, Karhunen-Loeve (KL), Hadamard, and independent-component features, quantifying feature fidelity in Gaussian-, shot-, and quantization-noise environments. An example of feature-specific imaging based on KL projections is analyzed and demonstrates that within a high-noise environment it is possible to improve image fidelity via direct feature measurement. A candidate optical system is presented and a preliminary implementational study is undertaken.
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Aapo Hyvärinen,Patrik O. Hoyer +1 more
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Stephen J. Roberts,Richard M. Everson +1 more
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TL;DR: This chapter discusses ICA: model order selection and dynamic source models William Penny, Stephen Roberts and Richard Everson, and its applications in data classification, visualisation and image processing.
Independent Component Analysis: Principles and Practice
TL;DR: Given the somewhat questionable organization of the book’s chapters, as well as the lack of smooth transitions between sections of this book, I recommend it for the experienced reader rather than the novice, even though a novice might find much of the advice valuable.
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