Due to the limited improvement of single-image based super-resolution (SR) methods in recent years, the reference based image SR (RefSR) methods, which super-resolve the low-resolution (LR) input with the guidance of similar high-resolution (HR) reference images are emerging. There are two main challenges in RefSR, i.e. reference image warping and exploring the guidance information from the warped references. For reference warping, we propose an efficient dense warping method to deal with large displacements, which is much faster than traditional patch (or texture) matching strategy. For the SR process, since different reference images complement each other, and have different similarities with the LR image, we further propose a similarity based feature fusion strategy to take advantage of the most similar reference regions. The SR process is realized by an encoder–decoder network and trained with pixel-level reconstruction loss, degradation loss and feature-level perceptual loss. Extensive experiments on three benchmark datasets demonstrate that the proposed method outperforms state-of-the-art SR methods in both subjective and objective measurements.

由于近年来基于单图像的超分辨率（SR）方法的改进有限，因此基于参考的图像SR（RefSR）方法可在类似的高分辨率图像的指导下超分辨低分辨率（LR）输入。分辨率（HR）参考图像正在出现。RefSR存在两个主要挑战，即参考图像变形和从变形的参考中探索指导信息。对于参考翘曲，我们提出了一种有效的密集翘曲方法来处理大位移，它比传统的贴片（或纹理）匹配策略快得多。对于SR过程，由于不同的参考图像相互补充，并且与LR图像具有不同的相似性，因此我们进一步提出了一种基于相似性的特征融合策略，以利用最相似的参考区域。SR过程是由编码器-解码器网络实现的，并经过像素级重建损失，降级损失和特征级感知损失的训练。在三个基准数据集上进行的大量实验表明，该方法在主观和客观测量方面均优于最新的SR方法。