Reconstructing Interpretable Features in Computational Super-Resolution microscopy via Regularized Latent Search
<|reference_start|>Reconstructing Interpretable Features in Computational Super-Resolution microscopy via Regularized Latent Search: Supervised deep learning approaches can artificially increase the resolution of microscopy images by learning a mapping between two image resolutions or modalities. However, such methods often require a large set of hard-to-get low-res/high-res image pairs and produce synthetic images with a moderate increase in resolution. Conversely, recent methods based on GAN latent search offered a drastic increase in resolution without the need of paired images. However, they offer limited reconstruction of the high-resolution image interpretable features. Here, we propose a robust super-resolution method based on regularized latent search~(RLS) that offers an actionable balance between fidelity to the ground-truth and realism of the recovered image given a distribution prior. The latter allows to split the analysis of a low-resolution image into a computational super-resolution task performed by deep learning followed by a quantification task performed by a handcrafted algorithm and based on interpretable biological features. This two-step process holds potential for various applications such as diagnostics on mobile devices, where the main aim is not to recover the high-resolution details of a specific sample but rather to obtain high-resolution images that preserve explainable and quantifiable differences between conditions.<|reference_end|>
@article{gheisari2024reconstructing,
    title={Reconstructing Interpretable Features in Computational Super-Resolution
  microscopy via Regularized Latent Search},
    author={Marzieh Gheisari and Auguste Genovesio},
    journal={arXiv preprint arXiv:2405.19112},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19112},
    primaryClass={eess.IV cs.CV}
}