HASN: hybrid attention separable network for efficient image super-resolution

Recently, lightweight methods for single-image super-resolution have gained significant popularity and achieved impressive performance due to limited hardware resources. These methods demonstrate that adopting residual feature distillation is an effective way to enhance performance. However, we find that using residual connections after each block increases the model’s storage and computational cost. Therefore, to simplify the network structure and learn higher-level features and relationships between features, we use depth-wise separable convolutions, fully connected layers, and activation functions as the basic feature extraction modules. This significantly reduces computational load and the number of parameters while maintaining strong feature extraction capabilities. To further enhance model performance, we propose the hybrid attention separable block, which combines channel attention and spatial attention, thus making use of their complementary advantages. Additionally, we use depth-wise separable convolutions instead of standard convolutions, significantly reducing the computational load and the number of parameters while maintaining strong feature extraction capabilities. During the training phase, we also adopt a warm-start retraining strategy to exploit the potential of the model further. Extensive experiments demonstrate the effectiveness of our approach. Our method achieves a smaller model size and reduced computational complexity without compromising performance. Code can be available at https://github.com/nathan66666/HASN.git