Enhanced implicit function-based network for arbitrary-scale image super-resolution

With the great development of deep learning, the performance of single image super-resolution (SR) has achieved tremendous progress. As an emerging and promising branch of the SR task, the arbitrary-scale SR task is receiving increasing attention from researchers due to its efficiency and practicality. Although the recent work learning implicit image function opened a solution for arbitrary-scale image SR, its reconstructed images contained structural distortions caused by defective prediction of high-frequency textures. To overcome this problem and further improve the performance of arbitrary-scale image SR, we propose an effective arbitrary-scale SR network, namely, enhanced arbitrary-scale super-resolution, which achieves the arbitrary-scale SR task in a single model by introducing a local-global encoder and enhanced implicit image function. Unlike conventional SR methods, which only stack up convolutional blocks to extract the local feature, the local-global encoder has two branches in parallel, including the local feature branch and the global prior branch. The former effectively extracts the local feature from a low-resolution image and the latter extracts the global prior to assist in the high-resolution image reconstruction. Next, we redesigned an enhanced implicit image function in the form of a dual modulation multiplayer perceptron (MLP) by replacing the implicit image function with a vanilla MLP. Moreover, we introduce the spatial encoding to further reduce structural distortions of reconstructed images. Extensive experiments were conducted to evaluate the performance and demonstrate the superiority of our proposed model.