Assessment of deep-learning-based resolution recovery algorithm relative to imaging system resolution and feature size
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Summary
This study evaluates a novel deep learning-based algorithm designed to overcome traditional limitations by learning a spatially varying point spread function from a set of registered low- and high-resolution image pairs, and shows excellent feature recovery.
- Type
- article
- Published
- 2025-02-11
- Cited by
- 6
- References
- 30
- Access
- Open access
- OpenAlex
- https://openalex.org/W4407342079
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:276271623
Keywords
Convolutional neural network, Computer science, Deconvolution, Artificial intelligence, Algorithm
References
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- Fully automated deep-learning-based resolution recovery
- Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning
- An Adaptive Richardson-Lucy Algorithm for Medical Image Restoration
- Exploring microstructure and petrophysical properties of microporous volcanic rocks through 3D multiscale and super-resolution imaging
- 3D and in situ Imaging of Electrochemical Energy Devices Powered by AI-driven X-ray Microscope Reconstruction Technologies.
- Performance of Deep Learning-based Image Denoising in Image Reconstruction for Various Acquisition Conditions: a Simulated Phantom Study.
- Machine Learning Super-Resolution of Laboratory CT Images in All-Solid-State Batteries using Synchrotron Radiation CT as Training Data
Cited by
- Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning
- Can the success of digital super-resolution networks be transferred to passive all-optical systems?
- Effective Super-Resolution X-ray Tomography using MSDnet for Nondestructive Testing of Metallic Lattices: Analysis of Training Dynamics and Strategies
- Super-resolution X-ray tomography using deep learning applied to the 3D quantification of defects in lattice structures
- Scalable Cloud Architecture for Computational Microscopy: A Microservices Approach with Dynamic Resource Management
- Accelerating in situ X-ray tomography using sparse projections and deep learning
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