Lightweight and accurate recursive fractal network for image super-resolution

Convolutional neural networks have recently achieved great success in image super-resolution (SR). However, we notice an interesting phenomenon that these SR models are getting bigger, deeper, and more complex. Extensive models promote the development of SR, but the effectiveness, reproducibility and practical application prospects of these new models need further verification. In this paper, we propose a lightweight and accurate SR framework, named Super-Resolution Recursive Fractal Network (SRRFN). SRRFN introduces a flexible and diverse fractal module, which enables it to construct infinitely possible topological sub-structure through a simple component. We also introduce the recursive learning mechanism to maximize the use of model parameters. Extensive experiments show that our SRRFN achieves favorable performance against state-of-the-art methods with fewer parameters and less execution time. All code is available at https://github.com/MIVRC/SRRFN-PyTorch.