LSFM DeStripe PyTorch

A PyTorch implementation of LSFM DeStripe method

Quick Start

Use as Python API

(1) Provide a filename, run slice by slice (suitable for extremely large file) suppose we have a to-be-processed volume saved in data_path with vertical stripe in it ```python from lsfm_destripe import DeStripe

exe = DeStripe() ###to run with default parameters for training out = exe.train( X1=datapath, isvertical=True, angleoffset=[0], mask=maskpath, ) where the volume in `mask_path` is to specifically indicate structures that are to be reserved after processing, left out can run prue DeStripe without constraint, `is_vertical` is to define whether the stirpes, or say the direction of light sheet propagation is along vertical or horizona. In practice, the stripe maight not be strictly vertical/horizontal, thus the angle offset in degree can be defined in `angle_offset`. Moreover, for multi-directional LSFM, for example Ultramicroscope II, Lavision Biotec. whose outputs exhibit stripes in 3 directions, -10 degrees, 0 degress, and 10 degrees, DeStripe can also remove all the stripes at the same time by runing: python out = exe.train( X1=datapath, isvertical=True, angleoffset=[-10, 0, 10], mask=maskpath, ) Alternatively, DeStripe can be initialized with user-defined train parameters, a full list of input arguments in `__init__` is here: losseps: float = 10 qr: float = 0.5 resampleratio: int = 3 GFkernelsizetrain: int = 29 GFkernelsizeinference: int = 29 hessiankernelsigma: float = 1 samplinginMSEloss: int = 2 isotropichessian: bool = True lambdatv: float = 1 lambdahessian: float = 1 inc: int = 16 nepochs: int = 300 wedgedegree: float = 29 nneighbors: int = 16 fastGF: bool = False requireglobalcorrection: bool = True fusionGFkernelsize: int = 49 fusionGaussiankernel_size: int = 49 device: str = None ```

(2) Provide a array, i.e., the image to be processed, and necessary parameters (more suitable for small data or for use in napari) suppose we have a to-be-processed volume img_arr that has been read in as a np.ndarray or dask.array.core.Array and has vertical stripes in it ```python from lsfm_destripe import DeStripe

run with default training params

out = DeStripe.trainonfullarr( X=imgarr, isvertical=True, angleoffset=[0], mask=maskarr, device="cuda", ) `` whereimgarrhas a size of $S \times 1 \times M \times N$,maskarrhas a size of $S \times M \times N$ with element 1 and 0 giving reserve or not respectively,deviceby default is "cpu". Also, customized training can be done by wrapping the training params that you'd like to change as a Dict.: ``python from lsfmdestripe import DeStripe

out = DeStripe.trainonfullarr( X=imgarr, isvertical=True, angleoffset=[0], mask=maskarr, device="cuda", trainparams = {"resample_ratio": 3}, ) ```

Run from command line for batch processing

suppose we have a to-be-processed volume saved in /path/to/my/image.tiff and it has vertical stripes, and we'd like to save the result from DeStripe as /path/to/save/result.tif bash destripe --X1_path /path/to/my/image.tiff \ --is_vertical True \ --angle_offset 0 \ --save_path /path/to/save/result.tif in addition, all the training parameters can be changed in command line: bash destripe --X1_path /path/to/my/image.tiff \ --is_vertical True \ --angle_offset 0,-10,10 \ --save_path /path/to/save/result.tif a full list of changeable args: usage: run_destripe --X1_path --save_path --is_vertical --angle_offset [--loss_eps 10] [--qr 0.5] [--resample_ratio 3] [--GF_kernel_size_train 29] [--GF_kernel_size_inference 29] [--hessian_kernel_sigma 1] [--sampling_in_MSEloss 2] [--isotropic_hessian "True"] [--lambda_tv 1] [--lambda_hessian 1] [--inc 16] [--n_epochs 300] [--wedge_degree 29] [--n_neighbors 16] [--fast_GF "False"] [--require_global_correction "True"] [--fusion_GF_kernel_size 49] [--fusion_Gaussian_kernel_size 49] [--X2_path None] [--mask_path None] [--boundary None] [--save_path_top_or_left_view None] [--save_path_bottom_or_right_view None]

Installation

Stable Release: pip install lsfm_destripe
Development Head: pip install git+https://github.com/peng-lab/lsfm_destripe.git

Development

See CONTRIBUTING.md for information related to developing the code.

MIT license