How does this skill calculate the illumination Numerical Aperture (NA)?

The skill calculates the lightsheet NA using the formula: sin(arctan(slitwidth / (2 * fcyl))), ensuring the PSF accurately reflects the optical setup.

Is this skill compatible with GPU acceleration?

Yes, it is specifically designed to work within the KINTSUGI KDecon module using CuPy for high-performance GPU processing on clusters like HiPerGator.

Why is the coordinate swap (z, 0, x) necessary?

In lightsheet systems, the excitation occurs perpendicular to the detection axis. The coordinate swap models this physical reality, whereas standard PSFs assume both excitation and emission share the same axis.

What causes horizontal banding in lightsheet deconvolution?

Banding artifacts typically occur when a deconvolution algorithm uses a widefield PSF that ignores lightsheet-specific parameters like slit width and focal length, failing to account for the physical illumination path.

Lightsheet PSF Deconvolution

Name: Lightsheet PSF Deconvolution
Author: smith6jt-cop

bysmith6jt-cop

Data Science & ML

Eliminates horizontal banding artifacts in microscopy data by implementing true lightsheet Point Spread Function (PSF) calculations.

About

The Lightsheet PSF Deconvolution skill provides a specialized implementation for the KINTSUGI KDecon module to correct visual artifacts in lightsheet microscopy images. By moving beyond standard widefield PSF assumptions, this skill incorporates critical parameters like cylinder lens focal length (fcyl) and slit aperture width to model the true physical illumination path. It implements essential coordinate transformations—specifically the (z, 0, x) swap—to accurately simulate the perpendicular nature of lightsheet illumination, ensuring high-fidelity image reconstruction in GPU-accelerated environments like HiPerGator.

Key Features

Orchestrates coordinate swaps (z, 0, x) for accurate excitation-emission physics
0 GitHub stars
Synchronizes Python implementation with validated MATLAB LsDeconv reference code
Implements true lightsheet PSF modeling using fcyl and slitwidth parameters
Corrects horizontal banding artifacts in Python deconvolution workflows
Provides GPU-accelerated processing via CuPy for large microscopy datasets

Use Cases

Porting validated MATLAB deconvolution logic to Python and GPU environments
Optimizing PSF generation for anisotropic lightsheet illumination
Fixing image artifacts in scientific microscopy datasets

About

Key Features

Orchestrates coordinate swaps (z, 0, x) for accurate excitation-emission physics
0 GitHub stars
Synchronizes Python implementation with validated MATLAB LsDeconv reference code
Implements true lightsheet PSF modeling using fcyl and slitwidth parameters
Corrects horizontal banding artifacts in Python deconvolution workflows
Provides GPU-accelerated processing via CuPy for large microscopy datasets

Use Cases

Porting validated MATLAB deconvolution logic to Python and GPU environments
Optimizing PSF generation for anisotropic lightsheet illumination
Fixing image artifacts in scientific microscopy datasets