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SHALLOW MULTI-SCALE NETWORK FOR STYLIZED SUPER-RESOLUTION

Thibault Durand 1 Julien Rabin David Tschumperlé
1 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : Image Super Resolution (SR) has come a long way since the early age of image processing. Deep learning methods nowadays give outstanding results, yet very few are actually used in digital illustration and photo retouching software due to large memory storage and GPU computational requirements, but also due to the actual lack of control provided to the user over the final result. This paper introduces a two-step framework for stylized SR using a multi-scale network built with independent parallel branches. The approach aims at : i.designing a shallow network based on image processing techniques making it usable on light hardware architecture (low memory cost, no GPU);ii.providing a versatile, controllable and customizable network to stylize SR results in a plug-and-play manner. We show that the proposed method offers significant advantages over state-of-the-art reference-based approaches regarding these aspects.
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https://hal.archives-ouvertes.fr/hal-03146958
Contributor : Thibault Durand <>
Submitted on : Friday, February 19, 2021 - 2:18:56 PM
Last modification on : Wednesday, March 10, 2021 - 3:29:47 AM

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  • HAL Id : hal-03146958, version 1

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Thibault Durand, Julien Rabin, David Tschumperlé. SHALLOW MULTI-SCALE NETWORK FOR STYLIZED SUPER-RESOLUTION. 2021. ⟨hal-03146958⟩

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