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Joint demosaicing and denoising by overfitting of bursts of raw images | | Thibaud Ehret
; Axel Davy
; Pablo Arias
; Gabriele Facciolo
; | | Date: |
13 May 2019 | | Abstract: | Demosaicking and denoising are the first steps of any camera image processing
pipeline and are key for obtaining high quality RGB images. A promising current
research trend aims at solving these two problems jointly using convolutional
neural networks. Due to the unavailability of ground truth data these networks
cannot be currently trained using real RAW images. Instead, they resort to
simulated data. In this paper we present a method to learn demosacking directly
from mosaicked images, without requiring ground truth RGB data. We apply this
to learn joint demosaicking and denoising only from RAW images, thus enabling
the use of real data. In addition we show that for this application overfitting
a network to a specific burst improves the quality of restoration for both
demosaicking and denoising. | | Source: | arXiv, 1905.5092 | | Services: | Forum | Review | PDF | Favorites | |
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