We propose a light-field microscopy display system that provides improved image quality and realistic three-dimensional (3D) measurement information. Our approach acquires both high-resolution two-dimensional (2D) and light-field images of the specimen sequentially. We put forward a matting Laplacian-based depth estimation algorithm to obtain nearly realistic 3D surface data, allowing the calculation of depth data, which is relatively close to the actual surface, and measurement information from the light-field images of specimens. High-reliability area data of the focus measure map and spatial affinity information of the matting Laplacian are used to estimate nearly realistic depths. This process represents a reference value for the light-field microscopy depth range that was not previously available. A 3D model is regenerated by combining the depth data and the high-resolution 2D image. The element image array is rendered through a simplified direction-reversal calculation method, which depends on user interaction from the 3D model and is displayed on the 3D display device. We confirm that the proposed system increases the accuracy of depth estimation and measurement and improves the quality of visualization and 3D display images.
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http://dx.doi.org/10.3390/s23042173 | DOI Listing |
Front Bioeng Biotechnol
November 2024
College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
Deep learning is progressively emerging as a vital tool for image reconstruction in light field microscopy. The present review provides a comprehensive examination of the latest advancements in light field image reconstruction techniques based on deep learning algorithms. First, the review briefly introduced the concept of light field and deep learning techniques.
View Article and Find Full Text PDFLight-field imaging is widely used in many fields, such as computer vision, graphics, and microscopy imaging, to record high-dimensional light information for abundant visual perception. However, light-field imaging systems generally have high system complexity and limited resolution. Over the last decades, lensless imaging systems have attracted tremendous attention to alleviate the restrictions of lens-based architectures.
View Article and Find Full Text PDFFluorescence microscopy has significantly advanced biological imaging at the nanoscale, particularly with the advent of super-resolution microscopy (SRM), which transcends the Abbe diffraction limit. Most cutting-edge SR methods require high-precision optical setups, which constrain the widespread adoption of SRM. Fluorescence fluctuation-based SRM (FF-SRM) can break the diffraction limit without complex optical components, making it particularly well-suited for biological imaging.
View Article and Find Full Text PDFLight Sci Appl
November 2024
State Key Laboratory of CNS/ATM & MIIT Key Laboratory of Complex-field Intelligent Sensing, Beijing Institute of Technology, No 5 Zhongguancun South Street, Haidian District, 100081, Beijing, China.
The event detection technique has been introduced to light-field microscopy, boosting its imaging speed in orders of magnitude with simultaneous axial resolution enhancement in scattering medium.
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