Benefiting from multitask learning to improve single image super-resolution
| Autor: | Claudiu Musat, Hazim Kemal Ekenel, Jean-Philippe Thiran, Max Basler, Mohammad Saeed Rad, Urs-Viktor Marti, Behzad Bozorgtabar |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Similarity (geometry) Pixel Computer science business.industry Cognitive Neuroscience ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Multi-task learning Pattern recognition 02 engineering and technology Convolutional neural network Computer Science Applications Image (mathematics) 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Artificial intelligence business Encoder Categorical variable |
| Zdroj: | Neurocomputing. 398:304-313 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2019.07.107 |
| Popis: | Despite significant progress toward super resolving more realistic images by deeper convolutional neural networks (CNNs), reconstructing fine and natural textures still remains a challenging problem. Recent works on single image super resolution (SISR) are mostly based on optimizing pixel and content wise similarity between recovered and high-resolution (HR) images and do not benefit from recognizability of semantic classes. In this paper, we introduce a novel approach using categorical information to tackle the SISR problem; we present 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. To explore categorical information during training, the proposed decoder only employs one shared deep network for two task-specific output layers. At run-time only layers resulting HR image are used and no segmentation label is required. Extensive perceptual experiments and a user study on images randomly selected from COCO-Stuff dataset demonstrate the effectiveness of our proposed method and it outperforms the state-of-the-art methods. |
| Databáze: | OpenAIRE |
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