G-IDRN: A Group-information Distillation Residual Network for Lightweight Image Super-resolution

Recently, most super-resolution algorithms based on deep learning have achieved satisfactory results. However, these methods generally consume large memory and have high computational complexity, and are difficult to apply to low computing power or portable devices. To address this problem, this paper introduces a lightweight group-information distillation residual network (G-IDRN) for fast and accurate single image super-resolution. Specially, we propose a more effective group-information distillation block (G-IDB) as the basic block for feature extraction. Simultaneously, we introduce dense shortcut to combine them to construct a group-information distillation residual group (G-IDRG), which is used to capture multi-level information and effectively reuse the learned features. Moreover, a lightweight asymmetric residual Non-local block is proposed to model the long-range dependencies and further improve the performance of super-resolution. Finally, a high-frequency loss function is designed to alleviate the problem of smoothing image details caused by pixel-wise loss. Extensive experiments show the proposed algorithm achieves a better trade-off between image super-resolution performance and model complexity against other state-of-the-art super-resolution methods and gets 56 FPS on the public test dataset B100 with a scale factor of 4 times, which is 15 times faster than the residual channel attention network. © 2024 Science Press. All rights reserved.