Over the past decade, many techniques for imaging systems at a resolution greater than the diffraction limit have been developed. These methods have allowed systems previously inaccessible to fluorescence microscopy to be studied and biological problems to be solved in the condensed phase. This brief review explains the basic principles of super-resolution imaging in both two and three dimensions, summarizes recent developments, and gives examples of how these techniques have been used to study complex biological systems.
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Deep learning-based study on assessment and enhancement strategy for geological disaster emergency evacuation capacity in Changbai Mountain North Scenic Area.
Sci Rep
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College of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China.
This study focuses on the northern scenic area of Changbai Mountain, aiming to evaluate the emergency evacuation capacity of the region in the context of geological disasters and to formulate corresponding improvement strategies. Due to the relatively small area of this region, difficulties in data acquisition, and insufficient precision, traditional models for evaluating emergency evacuation capacity are typically applied to urban built environments, with relatively few studies addressing scenic areas. To tackle these issues, this research employs the Real-Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN), which successfully resolves the problem of blurriness in remote sensing images and significantly enhances image clarity.
View Article and Find Full Text PDFGeneration of super-resolution images from barcode-based spatial transcriptomics by deep image prior.
Cell Rep Methods
December 2024
Portrai, Inc., Dongsullagil, 78-18 Jongrogu, Seoul, Republic of Korea; Department of Nuclear Medicine, Seoul National University Hospital, 03080 Seoul, Republic of Korea; Department of Nuclear Medicine, Seoul National University College of Medicine, 03080 Seoul, Republic of Korea. Electronic address:
Spatially resolved transcriptomics (ST) has revolutionized the field of biology by providing a powerful tool for analyzing gene expression in situ. However, current ST methods, particularly barcode-based methods, have limitations in reconstructing high-resolution images from barcodes sparsely distributed in slides. Here, we present SuperST, an algorithm that enables the reconstruction of dense matrices (higher-resolution and non-zero-inflated matrices) from low-resolution ST libraries.
View Article and Find Full Text PDFAssessment of microvascular flow in human atherosclerotic carotid plaques using ultrasound localization microscopy.
EBioMedicine
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Physics for Medicine Paris, INSERM U1273, ESPCI Paris, CNRS UMR 8063, PSL Research University, Paris, France.
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View Article and Find Full Text PDFDynamic Structure-Aware Modulation Network for Underwater Image Super-Resolution.
Biomimetics (Basel)
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School of Computer Information and Engineering, Nanchang Institute of Technology, Nanchang 330044, China.
Image super-resolution (SR) is a formidable challenge due to the intricacies of the underwater environment such as light absorption, scattering, and color distortion. Plenty of deep learning methods have provided a substantial performance boost for SR. Nevertheless, these methods are not only computationally expensive but also often lack flexibility in adapting to severely degraded image statistics.
View Article and Find Full Text PDFVolumetric Patch-Based Super-Resolution Reconstruction of Hyperpolarized C Cardiac MRI.
IEEE Access
November 2024
University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
The achievable spatial resolution of C metabolic images acquired with hyperpolarized C-pyruvate is worse than H images typically by an order of magnitude due to the rapidly decaying hyperpolarized signals and the low gyromagnetic ratio of C. This study is to develop and characterize a volumetric patch-based super-resolution reconstruction algorithm that enhances spatial resolution C cardiac MRI by utilizing structural information from H MRI. The reconstruction procedure comprises anatomical segmentation from high-resolution H MRI, calculation of a patch-based weight matrix, and iterative reconstruction of high-resolution multi-slice C MRI.
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