Frequency Recognition Algorithm for Multiple Exposures: Snapshot imaging using coded light

The central challenge tackled in this thesis is the development of an optical imaging approach capable of multidimensional image capture of dynamic samples. Many conventional optical imaging approaches, which can obtain dimensional information such as spectral, polarisation, or volumetric, to name a few, about samples either employ sequential image capture or parallelised detector arrangements. Due to the motion of dynamic samples the use of such imaging approaches encounter difficulties. Multidimensional information therefore needs to be obtained in a single exposure in order to avoid inaccurate image reconstruction, poor image overlap and artefacts due to motion blur.
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\noindent Snapshot techniques acquire multidimensional information in a single detector exposure and have been developed to meet the requirements for dynamic sample imaging. These approaches take many different forms, such as; the use of integrated camera sensor filter arrays, dispersive elements, lenslet arrays and coded light. In many of these cases there are constraints imposed, such as on the spectral resolution achievable or the number of unique images obtainable, by the manufacturing limits of the components required. In this thesis an alternative and novel snapshot imaging approach is presented which can intrinsically overcome some of the limitations of existing snapshot solutions available.
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\noindent This thesis presents a snapshot imaging method called FRAME (Frequency Recognition Algorithm for Multiple Exposures). FRAME uses spatial modulation patterns to encode different dimensional information about samples so that multiple images can be captured in a single camera exposure. Using a Fourier filtering approach the different encoded images can be isolated and extracted from the multiplexed image. As a result the approach is compatible with dynamic sample imaging. The work presented demonstrates the use of FRAME for multispectral, polarisation, extended depth of focus, temporal, and volumetric imaging. Additionally, convincing results exemplifying FRAMEs more notable attributes, are presented. These include the ability to distinguish florescence emissions resulting from spectrally close (3nm) excitation sources, successful distinction of simultaneously acquired strongly spectrally overlapping florescence signals from four fluorophores, and also from as many as nine different fluorophores. These latter features can be of great benefit for applications involving fluorescence imaging.