Implicitly Learned Neural Phase Functions for Basis-Free Point Spread Function Engineering
<|reference_start|>Implicitly Learned Neural Phase Functions for Basis-Free Point Spread Function Engineering: Point spread function (PSF) engineering is vital for precisely controlling the focus of light in computational imaging, with applications in neural imaging, fluorescence microscopy, and biophotonics. The PSF is derived from the magnitude of the Fourier transform of a phase function, making the construction of the phase function given the PSF (PSF engineering) an ill-posed inverse problem. Traditional PSF engineering methods rely on physical basis functions, limiting their ability to generalize across the range of PSFs required for imaging tasks. We introduce a novel approach leveraging implicit neural representations that significantly outperforms existing pixel-wise optimization methods in phase function quality.<|reference_end|>
@article{valouev2024implicitly,
    title={Implicitly Learned Neural Phase Functions for Basis-Free Point Spread
  Function Engineering},
    author={Aleksey Valouev},
    journal={arXiv preprint arXiv:2410.05413},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05413},
    primaryClass={physics.optics cs.CV}
}