2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDIMT-1.2
Paper Title SHALLOW MULTI-SCALE NETWORK FOR STYLIZED SUPER-RESOLUTION
Authors Thibault Durand, Julien Rabin, David Tschumperlé, Normandie University, France
SessionIMT-1: Computational Imaging Learning-based Models
LocationArea J
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Computational Imaging Methods and Models: Learning-Based Models
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
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.