Learning to Jointly Deblur, Demosaick and Denoise Raw Images

• URL: http://arxiv.org/abs/2104.06459v1
• Date: Tue, 13 Apr 2021 19:02:59 GMT
• Title: Learning to Jointly Deblur, Demosaick and Denoise Raw Images
• Authors: Thomas Eboli, Jian Sun and Jean Ponce
• Abstract summary: We adapt an existing learning-based approach to RGB image deblurring to handle raw images by introducing a new interpretable module. We train this model on RGB images converted into raw ones following a realistic invertible camera pipeline. We also apply our approach to remove a camera's inherent blur from real images, in essence deblurring sharp images.
• Score: 39.149955856017876
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We address the problem of non-blind deblurring and demosaicking of noisy raw images. We adapt an existing learning-based approach to RGB image deblurring to handle raw images by introducing a new interpretable module that jointly demosaicks and deblurs them. We train this model on RGB images converted into raw ones following a realistic invertible camera pipeline. We demonstrate the effectiveness of this model over two-stage approaches stacking demosaicking and deblurring modules on quantitive benchmarks. We also apply our approach to remove a camera's inherent blur (its color-dependent point-spread function) from real images, in essence deblurring sharp images.

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