DADE: difficulty-aware distillation-enhanced network for single image super-resolution

Abstract

Single image super-resolution (SISR) has achieved remarkable progress with deep neural networks, but the ever-growing model depth brings a heavy computational burden that limits practical deployment. Reducing the inference cost of SR networks has therefore become a central concern. A promising direction is to exploit the fact that image regions differ in restoration difficulty and to allocate computation accordingly. Existing content-adaptive methods have shown the potential of this idea, yet they typically rely on maintaining several separate sub-networks, so that the overall computational and storage cost remains high. In this paper, we propose DADE, a difficulty-aware distillation-enhanced network for efficient super-resolution. A single backbone is equipped with multiple intermediate experts corresponding to shallow, medium and deep computational paths that share their parameters, and a lightweight difficulty-aware classifier is jointly trained with the backbone to route each image patch to a suitable expert. To strengthen the shallow paths, we further introduce a knowledge-distillation scheme in which the deepest expert acts as a teacher and supervises the earlier experts during training. Extensive experiments on four standard benchmarks show that the proposed method substantially reduces the computational cost of super-resolution while maintaining reconstruction quality, with only negligible degradation in PSNR and SSIM, and at the same time greatly lowers the number of stored parameters.