Microscope Image Generation

This page explains how to generate realistic microscope images from simulated SMLM data and the available noise options.

Overview

SMLMSim can convert SMLD objects containing emitter coordinates into realistic microscope image stacks using point spread function (PSF) models. This functionality enables:

• Creating synthetic microscope data for algorithm testing
• Visualizing simulated molecules
• Generating training data for deep learning models
• Testing localization algorithms with realistic inputs

Basic Image Generation

The primary functions for creating images are:

• gen_images: Generate multiple frames (3D stack)
• gen_image: Generate a single frame (2D image)

Creating a Full Image Stack

To generate a complete image stack from an SMLD object:

using SMLMSim
using MicroscopePSFs

# Create or load an SMLD object
# (This could be from either static or diffusion simulation)
params = StaticSMLMParams(density=1.0, σ_psf=0.13, nframes=1000)
camera = IdealCamera(128, 128, 0.1)  # 128×128 pixels, 100nm pixels
pattern = Nmer2D(n=6, d=0.2)  # Hexamer with 200nm diameter
molecule = GenericFluor(1e4, [-50.0 50.0; 1e-2 -1e-2])  # Blinking model

# Run simulation
smld_true, smld_model, smld_noisy = simulate(params, pattern=pattern, molecule=molecule, camera=camera)

# Create a PSF model (Gaussian with 150nm width)
psf = GaussianPSF(0.15)  # 150nm PSF width

# Generate image stack from emitter data
images = gen_images(smld_model, psf; 
    bg=5.0,             # background photons per pixel
    poisson_noise=true  # add realistic photon counting noise
)

The resulting images is a 3D array with dimensions [height, width, frames] that can be used for visualization or algorithm development.

Creating a Single Frame

To generate an image for a specific frame:

# Generate image for frame 10
frame_image = gen_image(smld_model, psf, 10;
    bg=5.0,
    poisson_noise=true
)

Function Parameters

The gen_images function has the following signature:

gen_images(smld::SMLD, psf::AbstractPSF; kwargs...) -> Array{T, 3} where T<:Real

Required Parameters

• smld::SMLD: Single molecule localization data container
• psf::AbstractPSF: Point spread function model from MicroscopePSFs.jl

Optional Keyword Arguments

# Complete example with all available options
images = gen_images(smld_model, psf;
    dataset::Int=1,                # Dataset number to use from SMLD
    frames=nothing,                # Specific frames to generate (default: all frames)
    support=Inf,                   # PSF support region size in μm (Inf, scalar, or tuple)
    sampling=2,                    # Supersampling factor for PSF integration
    threaded=true,                 # Enable multithreading for faster computation
    bg=0.0,                        # Background signal level (photons per pixel)
    poisson_noise=false,           # Apply Poisson noise
    camera_noise=false             # Apply camera read noise (not yet implemented)
)

Noise Options

Realistic microscope images include various noise sources. SMLMSim supports the following options:

Photon Shot Noise

Photon shot noise follows a Poisson distribution and is the most fundamental noise source in fluorescence microscopy:

# Enable Poisson noise (default: false)
images = gen_images(smld_model, psf, poisson_noise=true)

When enabled, each pixel's intensity is drawn from a Poisson distribution with λ equal to the expected photon count.

Background Signal

Background noise can be added as a constant offset to all pixels:

# Add background signal (photons per pixel)
images = gen_images(smld_model, psf, bg=10.0)

When combined with Poisson noise, the background is included in the Poisson sampling process for a realistic noise model.

PSF Support Region

The PSF support region controls how much of the PSF is calculated for each emitter, affecting both accuracy and performance. This parameter is specified in microns (μm):

# Define a small support region (faster but may truncate PSF wings)
images = gen_images(smld_model, psf, support=3.0)  # 3.0 μm radius around each emitter

# Define a rectangular support region
images = gen_images(smld_model, psf, support=(3.0, 3.0, 1.0, 1.0))  # (left, right, bottom, top) in μm

# Use infinite support (most accurate but slowest)
images = gen_images(smld_model, psf, support=Inf)

Reducing the support region can significantly improve performance for large datasets at the cost of some accuracy. For most PSFs, a support radius of 3-5 times the PSF width (σ) is sufficient (typically 0.5-1.0 μm for a standard SMLM PSF).

Working with Image Stacks

The generated images can be visualized using various Julia plotting libraries:

using CairoMakie  # or GLMakie, etc.

# Display a single frame
fig = Figure()
ax = Axis(fig[1, 1], yreversed=true)
heatmap!(ax, images[:, :, 10]', colormap = :inferno)
display(fig)

# Create and save a movie from all frames
fig = Figure()
ax = Axis(fig[1, 1], yreversed=true)

# record will clear and redraw each frame for you
record(fig, "smlm_simulation.mp4", 1:size(images, 3); framerate = 10) do i
    heatmap!(ax, images[:, :, i]', colormap = :inferno)
end