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.
Original language | English |
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Journal | Visual Computer |
Early online date | 29 Feb 2024 |
DOIs | |
Publication status | E-pub ahead of print - 29 Feb 2024 |
Keywords
- Image super-resolution
- Attention mechanism
- Contrastive learning
- Deep learning