Maximum Likelihood Blind Deconvolution 3D Fluorescence Microscopy

9413037 Holmes本申请中提出的研究涉及通过基于盲解卷积算法的最大似然估计（MLE）的进一步开发、改进和评估来改进三维荧光显微镜成像。 具体地说，目标是进一步扩展，实施和测试的MLE盲反卷积算法的方式，将提供比其他去模糊算法上级降噪，缩短计算时间，提高可重复性。 这些算法将在神经生理学测试平台中进行评估。 图像将从通过其他成像手段充分研究的生物细胞系统中重建，但将受益于3D显微解剖学的改进可视化。 将评估所产生的3D图像显示3D结构的能力，以阐明特定神经生理学研究问题的答案。 ***

9413037 Holmes The research proposed in this application is concerned with improving three-dimensional fluorescence microscope imaging by further developing, refining, and evaluation of maximum likelihood estimates (MLE) based on blind deconvolution algorithms. Specifically, the objectives are to further extend, implement and test the MLE blind deconvolution algorithms in ways that will provide superior noise reduction over other deblurring algorithms, shorten computation time and improve repeatability. These algorithms will be evaluated in a neurophysiolgical testbed. Images will be reconstructed from biological cell systems that are well studied by other means of imaging, but will benefit from improved visualization of 3D microanatomy. The resulting 3D images will be evaluated for their ability to display 3D structures that elucidate answers to specific neurophysiological research questions. ***