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Optical coherence tomography (OCT) has stimulated a wide range of medical image-based diagnosis and treatment in fields such as cardiology and ophthalmology. Such applications can be further facilitated by deep learning-based super-resolution technology, which improves the capability of resolving morphological structures. However, existing deep learning-based method only focuses on spatial distribution and disregards frequency fidelity in image reconstruction, leading to a frequency bias. To overcome this limitation, we propose a frequency-aware super-resolution framework that integrates three critical frequency-based modules (i.e., frequency transformation, frequency skip connection, and frequency alignment) and frequency-based loss function into a conditional generative adversarial network (cGAN). We conducted a large-scale quantitative study from an existing coronary OCT dataset to demonstrate the superiority of our proposed framework over existing deep learning frameworks. In addition, we confirmed the generalizability of our framework by applying it to fish corneal images and rat retinal images, demonstrating its capability to super-resolve morphological details in eye imaging.
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Dynamic Laser Frequency Combs for Astronomical Spectrograph Characterization and Calibration
We demonstrate a dynamic frequency comb, which is tunable in both frequency and intensity, to characterize and calibrate an astronomical spectrograph. These capabilities are critical for achieving cm/s level radial velocity precision to detect Earth-analogs.
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- Award ID(s):
- 2009982
- PAR ID:
- 10656336
- Publisher / Repository:
- Optica Publishing Group
- Date Published:
- Page Range / eLocation ID:
- SS123_2
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1364/CLEO_SI.2025.SS123_2
