Image Simulation

This example demonstrates how to simulate the image formation process through an optical system, accounting for:

1. Spatially Variable Blur: Using EigenPSF decomposition to model PSF variation across the field.

2. Geometric Distortion: Warping the image based on the lens distortion map.

3. Lateral Color: Simulating wavelength-dependent magnification/distortion (if multi-wavelength).

We will use the ImageSimulationEngine class.

import numpy as np
import matplotlib.pyplot as plt
from optiland.samples.objectives import ReverseTelephoto
from optiland.analysis.image_simulation import ImageSimulationEngine

1. Lens Loading

We load a standard ReverseTelephoto lens, which has significant distortion and field-dependent aberrations.

optic = ReverseTelephoto()
_ = optic.draw()

2. Select Source Image

Note that the chosen image is based on HopemanMemorialCarillonRecitalSeries2018.jpg by DanielPenfield, used under CC BY-SA 4.0. Simulated images derived from this source are subject to the same license.

img_path = 'RushRheesLibrary_800x600.jpg'

3. Run Simulation

We configure the ImageSimulationEngine.

• wavelengths: Wavelengths to simulate (RGB).

• psf_grid_shape: Number of field points to sample for EigenPSFs (5x5 is usually sufficient).

• psf_size: Pixel size of the computed PSFs.

• num_rays: Ray density for PSF calculation (higher = more accurate but slower).

config = {
    "wavelengths": [0.65, 0.55, 0.45],  # RGB Simulation
    "psf_grid_shape": (5, 5),
    "psf_size": 128,
    "num_rays": 64,
    "oversample": 1
}

simulator = ImageSimulationEngine(optic, img_path, config=config)
result = simulator.run()

4. View Results

We compare the original and simulated images. A clear blur is visible in the simulated image, indicating the presence of aberrations.

_ = simulator.view()