Imaging through complex media is a well-known challenge, as scattering distorts a signal and invalidates imaging equations. For coherent imaging, the input field can be reconstructed using phase conjugation or knowledge of the complex transmission matrix. However, for incoherent light, wave interference methods are limited to small viewing angles. On the other hand, time-resolved methods do not rely on signal or object phase correlations, making them suitable for reconstructing wide-angle, larger-scale objects. Previously, a time-resolved technique was demonstrated for uniformly reflecting objects. Here, we generalize the technique to reconstruct the spatially varying reflectance of shapes hidden by angle-dependent diffuse layers. The technique is a noninvasive method of imaging three-dimensional objects without relying on coherence. For a given diffuser, ultrafast measurements are used in a convex optimization program to reconstruct a wide-angle, three-dimensional reflectance function. The method has potential use for biological imaging and material characterization.
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http://dx.doi.org/10.1364/JOSAA.31.000957 | DOI Listing |
Discov Nano
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Institute of Medical Research, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, Shaanxi, China.
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View Article and Find Full Text PDFEcol Evol
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Centro de Investigaciones sobre Desertificación CIDE CSIC-UVEG-GV Valencia Spain.
The spatial distribution pattern of plant species is frequently driven by a combination of biotic and abiotic factors that jointly influence the arrival, establishment, and reproduction of plants. Comparing the spatial distribution of a target plant species in different populations represents a robust approach to identify the underlying mechanisms. We mapped all reproductive individuals of the Iberian pear () in five plots (1.
View Article and Find Full Text PDFQuant Imaging Med Surg
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School of Computing, Mathematics and Engineering, Charles Sturt University, Albury, Australia.
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View Article and Find Full Text PDFQuant Imaging Med Surg
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School of Computer and Control Engineering, Yantai University, Yantai, China.
Background: Skin lesion segmentation plays a significant role in skin cancer diagnosis. However, due to the complex shapes, varying sizes, and different color depths, precise segmentation of skin lesions is a challenging task. Therefore, the aim of this study was to design a customized deep learning (DL) model for the precise segmentation of skin lesions, particularly for complex shapes and small target lesions.
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Comprehensive Cancer Center, Department of Oncology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, 321 Zhongshan Road, Nanjing, 210008, China.
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