A patch-based regression convolutional neural network for motion blur estimation

Luis G. Varela; Laura E. Boucheron; David Voelz; Abu Bucker Siddik; Steven Sandoval

doi:10.1117/12.2681497

3 October 2023 A patch-based regression convolutional neural network for motion blur estimation

Luis G. Varela, Laura E. Boucheron, David Voelz, Abu Bucker Siddik, Steven Sandoval

Proceedings Volume 12693, Unconventional Imaging, Sensing, and Adaptive Optics 2023; 126931F (2023) https://doi.org/10.1117/12.2681497
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Abstract

Non-uniform motion blur, including effects commonly encountered in blur associated with atmospheric turbulence, can be estimated as a superposition of locally linear uniform blur kernels. Linear uniform blur kernels are modeled using two parameters, length and angle. In recent work, we have demonstrated the use of a regression-based Convolutional Neural Network (CNN) for robust blind estimation of the length and angle blur parameters of linear uniform blur kernels. In this work we extend the approach of regression-based CNNs to analyze patches in images and estimate the parameters of a locally-linear motion blur kernel, allowing us to model the blur field. We analyze the effectiveness of this patch-based approach versus patch size for two problems: synthetic images generated as a superposition of locally linear blurs, and synthetic images generated with a Zernike polynomial-based wavefront distortion applied at the pupil plane.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Luis G. Varela, Laura E. Boucheron, David Voelz, Abu Bucker Siddik, and Steven Sandoval "A patch-based regression convolutional neural network for motion blur estimation", Proc. SPIE 12693, Unconventional Imaging, Sensing, and Adaptive Optics 2023, 126931F (3 October 2023); https://doi.org/10.1117/12.2681497

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