| Summary: | A culmination of theory, techniques and devices stemming from a wide variety of sources and disciplines, optical microscopy presents vast possibilities for visualisation of small structures. One of the most fundamental yet significant optical microscopy techniques is Confocal Fluorescence Microscopy (CFM). CFM is studied here by analysing its performance with respect to the two most important metrics - Signal-to-noise ratio and 3D optical resolution. Several authors have commented on the inherent inefficiency of imaging systems such as CFM to utilise the available light when providing resolution beyond the well-known diffraction limit, primarily due to the precise mechanisms that help realise the resolution gain in the first place. In CFM, the detection pinhole is the key mechanism that helps realise up to 1.4 times resolution improvement over conventional wide-field microscopy techniques by trading off SNR. First, an investigation of the inherent SNR-resolution trade-off in a CFM system is studied; the impact of the detection pinhole geometry on the performance of a CFM is examined by means of an effective trade-off curve. Using alternative pinhole geometries in conjunction with new detection schemes, it is next shown how performance gains are realised in both the lateral and axial directions. Examined next is a recently developed detection scheme called subtractive imaging; wherein a special annular pinhole is used to divide the confocal point spread function signal into two detectors. By using fast point detectors in place of CCD arrays, it is shown how using numerical optimisation yields an optimum “differential pinhole” to achieve considerable 3D resolution gains over conventional (circular pinhole based) CFM systems. By examining the trade-off curves it is also shown that the proposed design is able to offer simultaneous and maximum performance gains up to a considerably high SNR in comparison to conventional (circular pinhole) based CFM systems. Lastly, the work will propose the use of a deconvolution technique and an alternative detection scheme to demonstrate substantially higher improvements in the quality of images acquired by a CFM system. Image reconstruction is a tried and tested image post processing strategy to realise super resolution. An image reconstruction technique, based on an expectation maximisation maximum likelihood (EM-ML) algorithm is used in conjunction with array detectors to demonstrate enhanced resolution and noise performance of a CFM system. The point scan method used here renders the algorithm slow with long run times. To mitigate this, structured illumination is used to show how similar resolution gains in the array detector based CFM systems could be realised but in a much shorter time.
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