EZSN: efficient zero-shot network for blind super-resolution
Bo Qu, Lifen Jiang, Fengbo Zheng, Huazhi Sun · 2023
Most existing super-resolution (SR) methods are usually achieved through fully supervised means with massive training samples, and assume the degradation of low-resolution images corresponding to high-resolution images is fixed. However, the degradation process of real-world images is often more complex. Therefore, these models often perform poorly when dealing with low-resolution (LR) images with unknown degradation. In addition, runtime is also an important factor in deploying image super-resolution models, especially on devices with limited resources. However, the runtime of existing zero-shot blind super-resolution models is not ideal. In this paper, we propose an efficient zero-shot network (EZSN) for blind super-resolution. Specifically, we propose a high-frequency feature extraction block (HFFEB), which speeds up network inference by stacking highly optimized convolution and activation layers and reducing the use of feature fusion. In addition, we also propose an enhanced residual block (ERB) for extracting more features to improve model performance. The experimental results indicate that our proposed EZSN method has a significant advantage in terms of runtime compared to previous ZSSR methods when dealing with benchmark datasets that have unknown degradation.