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 diffi-cult to apply to low computing power or portable devices.To address this problem,this paper introduces a light-weight 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 ex-traction.Simultaneously,we introduce dense shortcut to combine them to construct a group-information distilla-tion residual group(G-IDRG),which is used to capture multi-level information and effectively reuse the learned fea-tures.Moreover,a lightweight asymmetric residual Non-local block is proposed to model the long-range dependen-cies 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 pro-posed 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.
Residual networksuper-resolutionfeature distillationhigh-frequency loss
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