Research on image super-resolution based on dense residual network and attention mechanism
To address the problem of insufficient feature information extraction in existing image super-resolution reconstruction algorithms,the hybrid attention modules and dense residual modules are introduced into the generator part of the SRResNet network to extract multi-scale features of images.The hybrid attention module integrates channel attention and self-attention mechanisms to focus on critical features.The dense residual module learns multi-level features by stacking multiple dense residual blocks and adopts improved dense connection method to improve feature reuse efficiency.The model achieves 0.1-1db improvement over current excellent reconstruction algorithms on various benchmark datasets,providing an effective solution for single image super-resolution tasks.
NETL
NSTL
万方数据
维普
