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default_parameters.yaml
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default_parameters.yaml
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image:
# path to input image (noisy)
path: null # str
# image and ground truth image are cropped to this region
crop_region: null # list[int]: [left, top, right, bottom]
# path to ground truth image
path_gt: null # str
# frames that are processed, default(null): all frames
frame_range: null # list[int]: [first, last + 1]
# number of runs
num_runs: 1 # int
# parameters for time-dependent images
time_series:
# use network from previous frame as initialization
is_series: false # bool
# learning rate after first image
learning_rate: 0.001 # float
# number of iterations after first image
num_iter: 30 # int
# apply time-dependent DECO-DIP also in the opposite way (last to first frame)
back_and_forth: true # bool
# network parameters
net:
# network type
net_type: skip # options: ResNet, skip, texture_nets, UNet, identity
# network input type
INPUT: noise # options: noise, meshgrid
# parameters that are optimized
OPT_OVER: net # str, options: net, down, input or combinations like: net,
# optimizer
optimizer: adam # options: LBFGS, adam
# regulates noise taht is added to the input image
reg_noise_std: 0 # float
# how to pad image for convolutions
pad: reflection # options: zero, reflection
# learning rate
learning_rate: 0.01 # float
# number of iterations
num_iter: 3 # int
# number of channels of the input image (usually random image)
input_depth: 32 # int
# number of channels of the noisy image
n_channels: 1 # int
# number of channels in each layer of the network (downwards direction)
# only for UNet and skip type nets
skip_n33d: 128 # int or list[int] with length = num_scales
# number of channels in each layer of the network (upwards direction)
# only for UNet and skip type nets
skip_n33u: 128
# number of channels in the skip connections for each layer of the network
# only for UNet and skip type nets
skip_n11: 4 # int or list[int] with length = num_scales
# depth of the network
num_scales: 8 # int
# mode to upsample images (on the way up in the network)
# only for UNet and skip type nets
upsample_mode: bilinear # options: nearest, bilinear
# # mode to downsample images (on the way down in the network)
# only for UNet and skip type nets
downsample_mode: stride # options: stride, avg, max, lanczos2
# activation function
act_fun: LeakyReLU # options: LeakyReLU, Swish, ELU, null
# calculate a exponential average over the outputs to make it smoother
exp_weight: 0 # float in [0, 1], the higher the smoother is the result
# number of iterations between checkpoints: save state and reset if output is not improved
checkpoint_interval: 100 # int
# configure superresolution output image
superresolution:
# upscaling factor, default 1 = no superresolution
superres_factor: 1 # int
# kernel to downsample image for comparison with original
downsample_kernel: lanczos2 # options: lanczos2, lanczos3, gauss12, gauss1sq2, lanczos, gauss, box
# configure the loss
loss:
# main loss type
loss_type_main: mse # options: mse, psf, rl
# optional second loss type for mixed loss
loss_type2: null # options: null, mse, psf, rl
# influence of the second loss
loss2_fact: 0.0 # float in [0, 1]
# decrease/increase the loss2 factor with every epoch
loss2_incr_fact: 0 # float
# optional explicit regularizer
regularizer: null # options: null, tv, tm
# impact of the regularizer
regularizer_fact: 0 # float
# decrease/increase the regularizer factor with every epoch
regularizer_incr_fact: 0
# configure the point spread function
psf:
experimental: false
# microscopy type
type: widefield # options: confocal, widefield
# excitation wave length in nm
lambdaEx: 561 # float
# emission wave length in nm
lambdaEm: 609 # float
# numerical aperture of the objective
numAper: 1.4 # float
# objective total magnification
magObj: 100 # int
# refractive index of the objective immersion medium
rindexObj: 1.518 # float
# pixel dimension of the CCD (in the plane of the camera)
ccdSize: 6540 # int
# optical axis Z sampling or defocusing in nm
dz: 0 # float
# size of the desired image (specimen view size/pixel dimension)
xysize: 384 # int
# number of slices desired (Depth view/Z axis sampling)
nslices: 1 # int
# depth of the specimen under the cover-slip in nm
depth: 0 # float
# refractive index of the specimen medium
rindexSp: 1.518 # float
# normalization on the PSF
nor: 0 # 0: l-infinity normalization, 1: l-1 normalization
# parameters for saving results and logging
save_and_log:
# path to an html file (will be created if it doesn't exist) to save images and
# corresponding parameters and loss results
image_html: results/results.html # str
# path to save the original image as png
orig_img_path: results/imgs/orig # str
# path to save the denoised image
denoised_img_path: results/imgs/denoised # str
# path to a csv file (will be created if it doesn't exist) to list parameters and
# corresponding losses
csv_path: null # str
# save loss for every iteration and result image for every 100th iteration
tensorboard: false # bool
# name of the tensorboard logdir
tensorboard_logdir: results/tensorboard/deco-dip # str
# verbosity
verbosity: 0 # int: 0 or 1