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Preprints, Working Papers, ... Year : 2021

Learning to Jointly Deblur, Demosaick and Denoise Raw Images

Abstract

We address the problem of non-blind deblurring and demosaicking of noisy raw images. We adapt an existing learningbased 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 colordependent point-spread function) from real images, in essence deblurring sharp images.
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Dates and versions

hal-03197462 , version 1 (13-04-2021)

Identifiers

  • HAL Id : hal-03197462 , version 1

Cite

Thomas Eboli, Jian Sun, Jean Ponce. Learning to Jointly Deblur, Demosaick and Denoise Raw Images. 2021. ⟨hal-03197462⟩
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