The objective of this project is to increase the effective resolution of optical light microscopes using deconvolution algorithms specifically designed for restoring imagery at a sub-pixel level. Light microscopy (LM) of living cells is typically diffraction limited to a maximum resolution of approximately 200nm, and resolution improvements are often considered to require hardware modifications such as structured illumination. The sub-pixel deconvolution software is intended to give life-science researchers an opportunity to increase the resolvability of fine structures within their 3D specimens, using their existing instrumentation and at a low cost. The sub-pixel algorithm is based on maximum-likelihood deconvolution and analytic continuation of photon-limited data. Particular importance is placed on developing algorithm acceleration techniques to reduce processing requirements and make the software commercially attractive. Methods will be investigated that model the effect of the camera pixel dimension and noise. Noise suppression methods are important to improve algorithm robustness in low-light imaging situations, and to retain fine features while preventing unwanted artifacts. The deconvolution will employ phase retrieval methods to estimate the wavefront error at the exit pupil of the microscope directly from the observed data. This approach to blind deconvolution will improve the ability of the algorithm to adapt the point spread function to subtle aberrations and tolerances in the objective lens specifications. Additionally, the sub-pixel algorithm will enable under-sampled imagery using on-chip camera binning or large pixels, and optical sections that are spaced beyond the Nyquist limit, to be correctly processed. The algorithms will be extended to be compatible with widefield fluorescence, transmitted light brightfield, spinning disk confocal and laser scanning ponfocal modalities. Performance will be verified using manufactured test targets and biological specimens with known structures. ? ?
