Summary of Drct: Saving Image Super-resolution Away From Information Bottleneck, by Chih-chung Hsu et al.
DRCT: Saving Image Super-resolution away from Information Bottleneck
by Chih-Chung Hsu, Chia-Ming Lee, Yi-Shiuan Chou
First submitted to arxiv on: 31 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In recent years, Vision Transformer-based approaches for low-level vision tasks have achieved widespread success. Unlike CNN-based models, Transformers are more adept at capturing long-range dependencies, enabling the reconstruction of images utilizing non-local information. The Swin-transformer has become mainstream in super-resolution due to its capability of global spatial information modeling and shifting-window attention mechanism. Many researchers have enhanced model performance by expanding receptive fields or designing meticulous networks, yielding commendable results. However, an information bottleneck is often observed towards the network’s end, limiting the model’s potential. To address this, we propose the Dense-residual-connected Transformer (DRCT) to mitigate the loss of spatial information and stabilize the information flow through dense-residual connections between layers. Our approach surpasses state-of-the-art methods on benchmark datasets and performs commendably at the NTIRE-2024 Image Super-Resolution (x4) Challenge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a breakthrough for image super-resolution, researchers have developed a new way to help models avoid getting stuck and perform better. The problem is that these models often lose important details as they process images, which can limit their ability to create high-quality results. To fix this, the team created a new type of model called the Dense-residual-connected Transformer (DRCT). This model helps prevent the loss of information by connecting different parts of the network together in a way that allows them to share information more effectively. The result is a model that can produce higher quality images than existing methods. |
Keywords
» Artificial intelligence » Attention » Cnn » Super resolution » Transformer » Vision transformer
