M
Max Basler
Researcher at Swisscom
Publications - 4
Citations - 138
Max Basler is an academic researcher from Swisscom. The author has contributed to research in topics: Pixel & Multi-task learning. The author has an hindex of 3, co-authored 4 publications receiving 69 citations.
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Proceedings ArticleDOI
SROBB: Targeted Perceptual Loss for Single Image Super-Resolution
Mohammad Saeed Rad,Behzad Bozorgtabar,Urs-Viktor Marti,Max Basler,Hazim Kemal Ekenel,Jean-Philippe Thiran +5 more
TL;DR: In this paper, the authors optimize a deep network-based decoder with a targeted objective function that penalizes images at different semantic levels using the corresponding terms, which results in more realistic textures and sharper edges.
Journal ArticleDOI
Benefiting from Multitask Learning to Improve Single Image Super-Resolution
Mohammad Saeed Rad,Behzad Bozorgtabar,Claudiu Musat,Urs-Viktor Marti,Max Basler,Hazim Kemal Ekenel,Hazim Kemal Ekenel,Jean-Philippe Thiran +7 more
TL;DR: Zhang et al. as discussed by the authors proposed an encoder architecture able to extract and use semantic information to super-resolve a given image by using multitask learning, simultaneously for image super-resolution and semantic segmentation.
Posted Content
Benefiting from Multitask Learning to Improve Single Image Super-Resolution
Mohammad Saeed Rad,Behzad Bozorgtabar,Claudiu Musat,Urs-Viktor Marti,Max Basler,Hazim Kemal Ekenel,Hazim Kemal Ekenel,Jean-Philippe Thiran +7 more
TL;DR: This paper presents a decoder architecture able to extract and use semantic information to super-resolve a given image by using multitask learning, simultaneously for image super-resolution and semantic segmentation, and outperforms the state-of-the-art methods.
Posted Content
SROBB: Targeted Perceptual Loss for Single Image Super-Resolution
Mohammad Saeed Rad,Behzad Bozorgtabar,Urs-Viktor Marti,Max Basler,Hazim Kemal Ekenel,Jean-Philippe Thiran +5 more
TL;DR: A deep network-based decoder with a targeted objective function that penalizes images at different semantic levels using the corresponding terms is optimized, which results in more realistic textures and sharper edges and outperforms other state-of-the-art algorithms.