Denoise 3D

usage: denoise3d [-h] [-o OUTPUT] [--suffix SUFFIX] [-m MODEL]
                 [-a EVEN_TRAIN_PATH] [-b ODD_TRAIN_PATH] [--N-train N_TRAIN]
                 [--N-test N_TEST] [-c CROP]
                 [--base-kernel-width BASE_KERNEL_WIDTH]
                 [--optim {adam,adagrad,sgd}] [--lr LR] [--criteria {L1,L2}]
                 [--momentum MOMENTUM] [--batch-size BATCH_SIZE]
                 [--num-epochs NUM_EPOCHS] [-w WEIGHT_DECAY]
                 [--save-interval SAVE_INTERVAL] [--save-prefix SAVE_PREFIX]
                 [--num-workers NUM_WORKERS] [-j NUM_THREADS] [-g GAUSSIAN]
                 [-s PATCH_SIZE] [-p PATCH_PADDING] [-d DEVICE]
                 [volumes ...]

Positional Arguments

volumes

volumes to denoise

Named Arguments

-o, --output

directory to save denoised volumes

--suffix

optional suffix to append to file paths. if not output is specfied, denoised volumes are written to the same location as the input with the suffix appended to the name (default .denoised)

-m, --model

use pretrained denoising model. accepts path to a previously saved model or one of the provided pretrained models. pretrained model options are: unet-3d, unet-3d-10a, unet-3d-20a (default: unet-3d)

Default: “unet-3d”

-a, --even-train-path

path to even training data

-b, --odd-train-path

path to odd training data

--N-train

Number of train points per volume (default: 1000)

Default: 1000

--N-test

Number of test points per volume (default: 200)

Default: 200

-c, --crop

training tile size (default: 96)

Default: 96

--base-kernel-width

width of the base convolutional filter kernel in the U-net model (default: 11)

Default: 11

--optim

Possible choices: adam, adagrad, sgd

optimizer (default: adagrad)

Default: “adagrad”

--lr

learning rate for the optimizer (default: 0.001)

Default: 0.001

--criteria

Possible choices: L1, L2

training criteria (default: L2)

Default: “L2”

--momentum

momentum parameter for SGD optimizer (default: 0.8)

Default: 0.8

--batch-size

minibatch size (default: 10)

Default: 10

--num-epochs

number of training epochs (default: 500)

Default: 500

-w, --weight_decay

L2 regularizer on the generative network (default: 0)

Default: 0

--save-interval

save frequency in epochs (default: 10)

Default: 10

--save-prefix

path prefix to save denoising model

--num-workers

number of workers for dataloader (default: 1)

Default: 1

-j, --num-threads

number of threads for pytorch, 0 uses pytorch defaults, <0 uses all cores (default: 0)

Default: 0

-g, --gaussian

standard deviation of Gaussian filter postprocessing, 0 means no postprocessing (default: 0)

Default: 0

-s, --patch-size

denoises volumes in patches of this size. not used if <1 (default: 96)

Default: 96

-p, --patch-padding

padding around each patch to remove edge artifacts (default: 48)

Default: 48

-d, --device

compute device/s to use (default: -2, multi gpu), set to >= 0 for single gpu, set to -1 for cpu

Default: -2