3 research outputs found
CycleISP: Real Image Restoration via Improved Data Synthesis
The availability of large-scale datasets has helped unleash the true
potential of deep convolutional neural networks (CNNs). However, for the
single-image denoising problem, capturing a real dataset is an unacceptably
expensive and cumbersome procedure. Consequently, image denoising algorithms
are mostly developed and evaluated on synthetic data that is usually generated
with a widespread assumption of additive white Gaussian noise (AWGN). While the
CNNs achieve impressive results on these synthetic datasets, they do not
perform well when applied on real camera images, as reported in recent
benchmark datasets. This is mainly because the AWGN is not adequate for
modeling the real camera noise which is signal-dependent and heavily
transformed by the camera imaging pipeline. In this paper, we present a
framework that models camera imaging pipeline in forward and reverse
directions. It allows us to produce any number of realistic image pairs for
denoising both in RAW and sRGB spaces. By training a new image denoising
network on realistic synthetic data, we achieve the state-of-the-art
performance on real camera benchmark datasets. The parameters in our model are
~5 times lesser than the previous best method for RAW denoising. Furthermore,
we demonstrate that the proposed framework generalizes beyond image denoising
problem e.g., for color matching in stereoscopic cinema. The source code and
pre-trained models are available at https://github.com/swz30/CycleISP.Comment: CVPR 2020 (Oral
Variational models for color image processing in the RGB space inspired by human vision Mémoire d'Habilitation a Diriger des Recherches dans la spécialité Mathématiques
La recherche que j'ai développée jusqu'à maintenant peut être divisée en quatre catégories principales : les modèles variationnels pourla correction de la couleur basée sur la perception humaine, le transfert d'histogrammes, le traitement d'images à haute gammedynamique et les statistiques d'images naturelles en couleur. Les sujets ci-dessus sont très inter-connectés car la couleur est un sujetfortement inter-disciplinaire