Convolutional blind-spot architectures and bayesian image restoration

A neural network architecture is disclosed for restoring noisy data. The neural network is a blind-spot network that can be trained according to a self-supervised framework. In an embodiment, the blind-spot network includes a plurality of network branches. Each network branch processes a version of...

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Bibliographic Details
Main Authors Aila, Timo Oskari, Lehtinen, Jaakko T, Laine, Samuli Matias, Karras, Tero Tapani
Format Patent
LanguageEnglish
Published 08.11.2022
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
HANDLING RECORD CARRIERS
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
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Summary:A neural network architecture is disclosed for restoring noisy data. The neural network is a blind-spot network that can be trained according to a self-supervised framework. In an embodiment, the blind-spot network includes a plurality of network branches. Each network branch processes a version of the input data using one or more layers associated with kernels that have a receptive field that extends in a particular half-plane relative to the output value. In one embodiment, the versions of the input data are offset in a particular direction and the convolution kernels are rotated to correspond to the particular direction of the associated network branch. In another embodiment, the versions of the input data are rotated and the convolution kernel is the same for each network branch. The outputs of the network branches are composited to de-noise the image. In some embodiments, Bayesian filtering is performed to de-noise the input data.
Bibliography:Application Number: US201916723375