Transcription of Deep Multi-Scale Convolutional Neural Network for …
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Deep Multi-Scale Convolutional Neural Network for …
Deep Multi-Scale Convolutional Neural Network for Dynamic Scene DeblurringSeungjun NahTae Hyun KimKyoung Mu LeeDepartment of ECE, ASRI, Seoul National University, 151-742, Seoul, Korea{ , blind deblurring for general dynamicscenes is a challenging computer vision problem as blursarise not only from multiple object motions but also fromcamera shake, scene depth variation. To remove thesecomplicated motion blurs, conventional energy optimiza-tion based methods rely on simple assumptions such thatblur kernel is partially uniform or locally linear. More-over, recent machine learning based methods also dependon synthetic blur datasets generated under these assump-tions. This makes conventional deblurring methods fail toremove blurs where blur kernel is dif cult to approximate orparameterize ( object motion boundaries). In this work,we propose a Multi-Scale Convolutional Neural Network thatrestores sharp images in an end-to-end manner where bluris caused by various sources.}
multi-scale architecture is used. Therefore, we make a multi-scale architecture that pre-serves fine-grained detail information as well as long-range dependency from coarser scales. Furthermore, we make sure intermediate level networks help the final stage in an explicit way by training network with multi-scale losses. 1.2.
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