Scale-Arbitrary Super-Resolution Based on Cross-Scale Implicit Feature Sensing

In recent years, significant progress has been made in single-image super-resolution reconstruction (SR) for integer scale factors, but in many practical applications (such as image editing and visual enhancement) non-integer SR and asymmetric SR are required. However, the performance of such networks needs to be improved. In this paper, we propose an image arbitrary scale super-resolution reconstruction network based on the Cross-Scale Implicit Feature Sensing Module (CIFS). Our module could easily capture long-range spatial dependencies that can greatly improve network performance during image reconstruction. Networks with CIFS inserted will have better SR performance. In addition, CIFS has the characteristics that the input dimension is equal to the output dimension, which is easy to integrate into other networks.