Fig. 1: Principles of SSAI-3D.
a SSAI-3D enables robust isotropic resolution recovery across diverse 3D imaging systems (confocal, light-sheet, wide-field, and nonlinear) and diverse 3D biological samples (organelles, cells, tissues, and organs). Created in BioRender. Liu, K. (2024) BioRender.com/f26y695. b The deblurring network is initialized with a large pre-trained network for blind deconvolution on a large dataset of natural image pairs. Example images were sourced from the REDS dataset52, released under a CC BY 4.0 license, which permits commercial use with proper attribution. c Starting with a single microscopy-specific image stack where the axial resolution is worse than the lateral resolution, lateral images are blurred with a series of PSFs of different sizes and orientations to generate a self-supervised dataset. Then, generating zero-shot metrics using ~1% of this dataset, a surgeon network is employed to select the critical layers to fine-tune in the large pre-trained deblurring network. Only ~10% layers are selected and sparsely fine-tuned according to the generated self-supervised dataset. Given unseen axial images, the fine-tuned deblurring network predicts deblurred images with isotropic resolution. Insets in the images represent the corresponding Fourier spectrums.
