Core Concepts
A novel super-resolution reconstruction algorithm that achieves significant accuracy improvement through a unique design while maintaining low computational complexity.
Abstract
The paper introduces a deep learning-based algorithm for efficient single image super-resolution reconstruction. The core of the algorithm lies in the integration of two key modules:
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Global-Local Information Extraction Module:
- Combines global and local information to provide a comprehensive understanding of the image content.
- Expands the receptive field and fuses local details with global context, enabling accurate reconstruction of both global structures and local textures.
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Basic Block Module:
- Consists of a Spatial Channel Adaptive Modulation (SCAM) module and a Channel Fusion Convolution (CFC) module.
- The SCAM module adaptively adjusts features in both spatial and channel dimensions, enhancing the flexibility and effectiveness of feature extraction.
- The CFC module efficiently encodes spatially localized information and improves feature interaction capability.
The algorithm achieves state-of-the-art performance on various benchmark datasets, outperforming existing CNN-based and Transformer-based methods in terms of both accuracy and computational complexity. Extensive experiments and ablation studies demonstrate the effectiveness of the proposed global-local information synergy and the core modules.
Stats
The model in this paper has 46% fewer parameters and 62% less computation compared to SRFormer.
The model in this paper has 91% fewer parameters and 92% less computation compared to HAN.
The model in this paper has 64% fewer parameters and 66% less computation compared to RCAN.
The model in this paper has 86% fewer parameters and 86% less computation compared to EDSR.
Quotes
"The core of the algorithm lies in the clever integration of the global-local information extraction module and the basic Block module, which work together to realize the reconstruction of high-quality images."
"The global-local information extraction module is able to capture all kinds of information in the image in a comprehensive and in-depth manner, whether it is global structural features or local texture details, all of which can be accurately extracted."
"The basic Block module is another core in the algorithm. It combines the two techniques of spatial channel adaptive modulation and hybrid channel convolution, which enhances the flexibility of the algorithm and improves the efficiency of feature extraction."