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Novel high-fidelity computational microscopy uses stable features for clearer imaging
Computational microscopy is vital in biomedicine and materials science. Traditional methods struggle with optical aberrations, noise interference, and differences between physical models and real-world imaging, reducing the resolution and accuracy. They rely on pixel-level optimization, which fails to maintain high-quality imaging in complex environments. Therefore, developing a precise and stable computational imaging approach has become a research focus.
Computational microscopy is vital in biomedicine and materials science. Traditional methods struggle with optical aberrations, noise interference, and differences between physical models and real-world imaging, reducing the resolution and accuracy. They rely on pixel-level optimization, which fails to maintain high-quality imaging in complex environments. Therefore, developing a precise and stable computational imaging approach has become a research focus.
