Laser Protection via Jointly Learned Defocus and Image Reconstruction
Year: 2025
Type: article
Abstract: We propose a method to harden sensors against laser radiation by defocusing the employed optics on purpose, and to reconstruct the sought focused images of the scene via image reconstruction. The introduced defocus widens the laser spot incident on the sensor and greatly reduces its damage potential. We employ a coded aperture and optimize its pattern jointly with the free parameters of the image reconstruction pipeline. For the image reconstruction, we combine a state-of-the-art alternating direction method of multipliers (ADMM)-based physically informed deconvolution stage with a U-Net-like neural network to remove remaining reconstruction artifacts. To evaluate the performance of our proposed approach, we conducted reconstruction experiments on simulated data, including ablation experiments and on real data and performed sensor destruction tests with and without sensor protection. Destructive experiments with increasing laser power suggest that our approach has the potential to increase the tolerable radiation threshold by about three orders of magnitudes.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,645
OpenAlex ID: W4412372173