Lightweight image super-resolution network based on muti-domain information enhancement

Aiming to solve the problems that the reconstruction capability of single-domain features was limited and deep convolutional neural networks used in existing single-image super-resolution reconstruction tasks were difficult to deploy on mobile terminals due to the large number of parameters and high computational requirements, a lightweight image super-resolution network based on multi-domain information enhancement was proposed. Initiating from three dimensions, a set of innovative techniques had been developed, including multi-path large kernel feature extraction in the spatial domain, local information enhancement attention, frequency-domain feature enhancement through frequency splitting, and transformation-domain prior-guided high-frequency feature simulation. By processing information across different feature domains, both global and local low-frequency and high-frequency features were optimized, significantly improving the model’s performance in detail recovery and image reconstruction. Extensive experimental comparisons and analyses with the existing advanced algorithms on the recognized benchmark datasets demonstrate that the proposed network model can achieve remarkable reconstruction results while enjoying a high trade-off between performance and efficiency.