February 19, 2024

Spectrum learning for super-resolution tomographic reconstruction

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Resumen

. Computed Tomography (CT) has been widely used in industrial high-resolution non-destructive testing. However, it is difficult to obtain high-resolution images for large-scale objects due to their physical limitations. The objective is to develop an improved super-resolution technique that preserves small structures and details while efficiently capturing high-frequency information.

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Li et al. (Mon,) studied this question.

synapsesocial.com/papers/68e78968b6db6435876fbf52 https://doi.org/https://doi.org/10.1088/1361-6560/ad2a94

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Synapse has enriched 4 closely related papers on similar clinical questions. Consider them for comparative context:

1Super-resolution x-ray phase-contrast and dark-field imaging with a single 2D grating and electromagnetic source stepping2019 · 15 citations
2CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE)2019 · 453 citations
3Deep Embedding-Attention-Refinement for Sparse-View CT Reconstruction2022 · 79 citations
4Super-resolution reconstruction of MR image with a novel residual learning network algorithm2018 · 117 citations

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