Applied Vision

Spot and Edge Feature Based Estimation of Point-Spread Functions for Image Deconvolution

Author: Martin Welk (UMIT TIROL)

Abstract

We consider the extraction of point-spread function (PSF) information for blind image
deconvolution from blurred images in a way that preserves phase information,
in contrast to using cues like autocorrelation that reveal only spectral information.
Our approach is based on extracting suitable feature patches, depending on the type
of images either spot highlights or edge segments. We discuss how edge patches in
fact constitute a tomographic representation of the PSF. In integrating information
from spot or edge patches into a PSF estimate, it is essential to compensate spatial
misalignments. We achieve this by an iterative update rule that combines Fourier
transformation with a nonlinear intensity transformation to achieve shift invariance.
Although designed with the goal of integration into alternating minimisation
schemes, the two-step procedure of PSF estimation followed by non-blind deconvolution
developed here performs surprisingly well as a fast blind deconvolution
method in its own right on suitable image classes.

Keywords:

How to Cite: Welk, M. (2026) “Spot and Edge Feature Based Estimation of Point-Spread Functions for Image Deconvolution”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision. 3(1).