Leveraging Phase Information to Boost Unrolled Network Learning for Image Deblurring

arXiv cs.CV1mo4 min read

arXiv:2607.00251v1 Announce Type: new Abstract: While most image deblurring techniques directly restore the spatial image variable, we propose an amplitude and phase decomposition recognizing the importance of accurate phase estimation in recovering sharp image details. To that end, we first develop novel linear minimum mean squared (LMMSE) estimators of the amplitude and phase of the blurred, noisy image observation. An iterative optimization algorithm follows that recovers the sharp image using the aforementioned LMMSE estimators. Finally, matrix parameters that are statistically determined

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Leveraging Phase Information to Boost Unrolled Network Learning for Image Deblurring

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