Abstract: With the development of convolutional neural networks, image super-resolution reconstruction algorithms have made some breakthroughs. Nevertheless, the existing image super-resolution algorithms rarely distinguish the use of hierarchical features and suffer from the problem of costly multi-scale feature extraction. To address these problems, this paper proposes an image super-resolution reconstruction algorithm with adaptive aggregation of hierarchical information. Specifically, the algorithm applies a multi-level information refinement mechanism for the adaptive enhancement of features at different levels to solve the problem that the hierarchical features are not distinguishably utilized. In addition, it is proposed to construct a fine-grained multi-scale information aggregation block to solve the problem of costly multi-scale information extraction and poor feature representation capability. Finally, the algorithm focuses on contrast-enhanced recombinant attention blocks to achieve adaptive calibration of features at a lower cost by exploiting channel and spatial information. Extensive experiments show that compared with other advanced algorithms, the proposed method can achieve better metrics and visual results on five benchmark datasets such as Urban100.

Key words: super-resolution, multi-level information refinement, multi-scale information, recombinant attention

摘要： 随着卷积神经网络的发展，图像超分辨率重建算法取得了一定的突破。尽管如此，现有的图像超分辨率算法很少区分利用层次特征，并且存在多尺度特征提取代价大的问题。针对这些问题，提出了层次信息自适应聚合的图像超分辨率重建算法。具体来说，采用多层次信息精炼机制对不同层次的特征进行自适应增强处理，解决层次特征没有区分利用的问题。构造细粒度的多尺度信息聚合块，解决多尺度信息提取代价大，特征表征能力弱的问题。提出对比度增强的重组注意力块，以较低的代价同时利用特征的通道和空间信息，实现对特征的自适应校准。大量实验表明，相比其他先进的算法，所提方法在Urban100等五个基准数据集上能取得更好的指标和视觉效果。

关键词: 超分辨率, 多层次信息精炼, 多尺度信息, 重组注意力