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High-frequency channel attention and contrastive learning for image super-resolution.
- Source :
-
Visual Computer . Feb2024, p1-13. - Publication Year :
- 2024
-
Abstract
- Over the last decade, convolutional neural networks (CNNs) have allowed remarkable advances in single image super-resolution (SISR). In general, recovering high-frequency features is crucial for high-performance models. High-frequency features suffer more serious damages than low-frequency features during downscaling, making it hard to recover edges and textures. In this paper, we attempt to guide the network to focus more on high-frequency features in restoration from both channel and spatial perspectives. Specifically, we propose a high-frequency channel attention (HFCA) module and a frequency contrastive learning (FCL) loss to aid the process. For the channel-wise perspective, the HFCA module rescales channels by predicting statistical similarity metrics of the feature maps and their high-frequency components. For the spatial perspective, the FCL loss introduces contrastive learning to train a spatial mask that adaptively assigns high-frequency areas with large scaling factors. We incorporate the proposed HFCA module and FCL loss into an EDSR baseline model to construct the proposed lightweight high-frequency channel contrastive network (HFCCN). Extensive experimental results show that it can yield markedly improved or competitive performances compared to the state-of-the-art networks of similar model parameters. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01782789
- Database :
- Academic Search Index
- Journal :
- Visual Computer
- Publication Type :
- Academic Journal
- Accession number :
- 175739607
- Full Text :
- https://doi.org/10.1007/s00371-024-03276-8