Sub-Diffraction and Sub-Pixel Microscopic Deconvolution

亚衍射和亚像素显微反卷积

• 批准号: 7285666
• 负责人: TIMOTHY J HOLMES
• 金额: $ 28.17万
• 依托单位: LICKENBROCK TECHNOLOGIES, LLC
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-08-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7285666
• 关键词: Acceleration; Algorithms; Biological; Biological Sciences; Bite; Cells; Compatible; Complex; Computer Simulation; Computer Systems; Computer software; Data; Data Set; Development; Dimensions; Electron Microscope; Electron Microscopy; Financial compensation; Fluorescence; Gold; Image; Imagery; Lasers; Libraries; Life; Light; Light Microscope; Lighting; Methods; Microscope; Microscopic; Modality; Modeling; Modification; Morphologic artifacts; Noise; Operating System; Optics; Performance; Phase; Photons; Process; Pupil; Reporting; Research; Research Personnel; Research Project Grants; Resolution; Retrieval; Sampling; Scanning; Specimen; Speed; Standards of Weights and Measures; Structure; Techniques; Technology; Testing; Time; base; blind; cost; design; image processing; improved; instrumentation; lens; light microscopy; prevent; reconstruction; research study; restoration; size

DESCRIPTION (provided by applicant): 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.

描述（由申请人提供）：该项目的目的是使用专门设计用于在子像素级别恢复图像的反卷积算法来增加光学显微镜的有效分辨率。活细胞的光显微镜（LM）通常衍射仅限于大约200nm的最大分辨率，并且通常认为分辨率改进需要硬件修改，例如结构化照明。子像素反卷积软件旨在为生命科学研究人员提供一个机会，以使用现有的仪器和低成本来提高其3D标本中精细结构的可分辨性。子像素算法基于光子限制数据的最大可能性反向卷积和分析延续。特别重要的是开发算法加速技术以减少处理要求并使软件具有商业上的吸引力。将研究方法，以模拟相机像素维度和噪声的效果。抑制噪声方法对于在低光成像情况下改善算法鲁棒性非常重要，并保留精细的特征，同时防止不必要的人工制品。反卷积将采用相位检索方法来直接从观察到的数据直接从显微镜的出口瞳孔估算波前误差。这种盲目反卷积的方法将提高算法使点扩散函数适应物镜规范中微妙的畸变和公差的能力。此外，子像素算法将使用片上摄像头或大像素构建不采样的图像，以及超出Nyquist限制的光学部分，以正确处理。该算法将扩展到与广阔的荧光，透射的明亮场，旋转磁盘共聚焦和激光扫描ponfocal模式兼容。将使用具有已知结构的生产测试目标和生物标本来验证性能。