Extraction of 3D Point Spread Function from in-vivo Two-Photon Microscope Acquisition
Two-photon microscopy image volumes often suffer from inherent blur due to optical imperfections. Although theoretical point spread function (PSF) estimates can be derived from optical parameters, real-world PSFs often deviate due to misalignments or tissue-induced wavefront distortions. We introduce a novel three-dimensional blind deconvolution approach that integrates both data-consistency and guidance terms.
Output Files
After the results are ready, we will email you a link to a zip file with the following content:
├── log_deconvolution.out ├── errors_with_request.log └── final_results ├── centered_max_int_final_kernel_estimate_centered.tif ├── final_kernel_estimate_centered_mips.png ├── final_kernel_estimate_centered.tif ├── final_kernel_estimate_mips.png ├── final_kernel_estimate.tif ├── kernel_estimate_evolution.gif ├── non_blind_deconv │ └── final_kernel_estimate_centered │ ├── full_volume │ │ └── deconwolf │ │ └── deconvolved_DW__.tif │ └── patches │ └── deconwolf │ ├── deconvolved_DW__.tif │ └── ... ├── kernel_estimate │ ├── step_0000 │ │ ├── auxiliary_kernel__step_00_actual_values.tif │ │ ├── auxiliary_kernel__step_00_seg_mask_0.1_threshold__only_viz.tif │ │ ├── auxiliary_kernel__step_00_xy_xz_mip.png │ │ ├── auxiliary_kernel__step_00_xy_yz_mip.png │ │ ├── kernel_estimate__step_00_actual_values.tif │ │ ├── kernel_estimate__step_00_seg_mask_0.1_threshold__only_viz.tif │ │ ├── kernel_estimate__step_00_xy_xz_mip.png │ │ └── kernel_estimate__step_00_xy_yz_mip.png │ └── step_XXXX │ ├── ... ├── blurred_patches │ ├── patch_0.tif │ └── ... ├── sharp_patches │ ├── patch_0.tif │ └── ... ├── loss_history.csv ├── loss_history_HQS_1st_half.png ├── loss_history_HQS_2nd_half.png └── loss_history.png