Multi-task learning for image restoration

dc.contributor.author	Martyniuk, Tetiana
dc.date.accessioned	2019-02-19T14:47:21Z
dc.date.available	2019-02-19T14:47:21Z
dc.date.issued	2019
dc.identifier.citation	Martyniuk, Tetiana. Multi-task learning for image restoration : Master Thesis : manuscript / Tetiana Martyniuk ; Supervisor Orest Kupyn ; Ukrainian Catholic University, Department of Computer Sciences. – Lviv : [s.n.], 2019. – 26 p. : ill.	uk
dc.identifier.uri	http://er.ucu.edu.ua/handle/1/1332
dc.language.iso	en	uk
dc.subject	multi-task learning	uk
dc.subject	image restoration	uk
dc.title	Multi-task learning for image restoration	uk
dc.type	Preprint	uk
dc.status	Публікується вперше	uk
dc.description.abstracten	We present an efficient end-to-end pipeline for general image restoration. The setting has a generic encoder and separate decoders so that our model can benefit from the shared low-level feature representations between the tasks. We also introduce the new architecture for the generator inspired by the feature pyramid networks for dealing with multi-scale degradations. We train the models for solving three particular image restoration problems: deblurring, dehazing, and raindrop removal.	uk