CRAN: Compressed Residual Attention Network for Lightweight Single Image Super-Resolution

초록

With the growing demand for edge device and mobile environment, the development of lightweight super-resolution (SR) is necessary. However, existing SR approaches have primarily focused on increasing model performance, leading to high computational cost and reliance on high-performance GPUs. To address the issue, we propose a Compressed Residual Attention Network (CRAN), a lightweight SR model designed for high efficiency and performance. CRAN incorporates a Compressed Linear Block (CLB) and a Compressed Residual Attention Block (CRAB), utilizing linear overparameterization and spatial attention mechanism to maximize representational capacity while minimizing computational complexity. Additionally, a feature connection (FC) mechanism is proposed to enhance feature propagation through overparameterized layers without increasing parameter counts. Comprehensive experiments on SR benchmark datasets demonstrate that CRAN achieves superior results compared to existing lightweight SR methods, offering an excellent trade-off between efficiency and performance.

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