Blind multi-frame deconvolution for the correction of space-variant blur in images

TL;DR

This paper presents a practical blind multi-frame deconvolution method that jointly estimates object and spatially varying PSFs to correct space-variant blur in images, demonstrating robustness to noise and large PSF translations.

Contribution

It introduces a novel joint estimation algorithm that effectively handles large translations and spatial variations in PSFs, improving deblurring accuracy over existing methods.

Findings

Successfully corrects space-variant blur in numerical simulations.

Demonstrates robustness to noise and large PSF translations.

Outperforms a state-of-the-art method in experiments.

Abstract

This paper demonstrates a practical method that can correct spatial varying blur from a set of images of the same object. The algorithm jointly estimates the object and local point spread functions~(PSF). The method prioritizes sections with small spatial variation in the PSF for deconvolution. This novel approach can handle large translations in the local PSFs, hence the algorithm is able to correct for morph in the images. Robustness to noise is demonstrated in numerical simulations. Numerical experiments are conducted where the performance of the algorithm is compared to a state-of-the-art method found in literature. The algorithm can be used in situation with space-temporal variation of the PSF and can be applied in situations where the signal-to-noise ratio is low.