point-spread-function

Python2 (OpenCV, NumPy) application to refocus blurred images using Wiener deconvolution.

PSF Utilities

This project deals with blind motion deblurring using a combination of Weiner Deconvolution and Deep Learning techniques to estimate the length and angle parameter of the Point-Spread Function

TOPH is a code for spatially-variable point spread function matching.

Fitting distributions to discrete stellar profiles in FITS data using LGD.

Python package to create synthetic (fluorescence) microscopy images of (nano)particles and convolution with a point spread function

非盲图像复原与图像复原

“Disparitybased space-variant image deblurring,” Signal Processing: Image Communication, vol. 28, no. 7, pp. 792–808, 2013.

Point Spread Function calculations for fluorescence microscopy.

Python CLI for batch image deblurring, image restoration, deconvolution, and classical computer vision. Includes Wiener, Richardson-Lucy, Tikhonov, TV-ADMM, and unsupervised Wiener methods; motion/Gaussian/box/custom PSFs; recursive OpenCV/scikit-image processing; color/grayscale support, edge padding, presets, and JSON reports.

Non-generative, physics-informed neural deblurring: deep-unfolded Wiener deconvolution + a Lipschitz-constrained learned denoiser, driven by real camera EXIF/optics -- now with diffraction-limited Airy-disk PSFs, per-RGB-channel chromatic dispersion, and depth-guided spatially variant deconvolution.

Physics-based synthetic data and defocus-regression models for fluorescence microscope autofocus: Richards-Wolf vectorial PSF simulation, 14 sample geometries, a full sCMOS/EMCCD sensor model, geometry-invariant focus features, and the classical z-scan baselines to beat.

A Python package for manipulating and correcting variable point spread functions.

Euclid stellar-shape and PSF diagnostic for image-quality assessment, weak-lensing calibration and shape-measurement consistency, using reproducible analysis of observational data and explicit quality limits.