Recently, deep learning methods have achieved excellent performance in single image super-resolution (SISR), but most existing methods suffer from heavy computational costs and memory storage. To address this problem, some lightweight SR methods have been proposed, among which convolutional neural network (CNN) with attention mechanisms has received increasing attention. However, the existing CNN-based lightweight network and attention mechanism still have redundancy in feature extraction and enhancement. This paper presents Efficient Self-calibrated and Hierarchical Refinement Network (ESHR) for Lightweight Super-Resolution that includes three effective designs. First, we propose a more effective feature extraction basic module called Self-calibrated Residual Block (SRB), which can achieve superior performance when replacing other common convolution modules such as traditional convolution, BSConv and DSConv. Second, we propose a more lightweight spatial attention mechanism called Squeeze-Excitation Spatial Attention (SESA), which can interact and enhance features more effectively in spatial dimensions to improve the representation ability of the model. Third, we design an Efficient Hierarchical Refinement Block (EHRB) to integrate and utilize multi-level features, which can achieve better performance than single-level refinement without increasing the number of parameters. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art CNN-based methods quantitatively and qualitatively.

中文翻译：

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用于轻量级超分辨率的高效自校准和分层细化网络

最近，深度学习方法在单图像超分辨率（SISR）方面取得了优异的性能，但大多数现有方法都面临着沉重的计算成本和内存存储的问题。为了解决这个问题，一些轻量级的SR方法被提出，其中具有注意力机制的卷积神经网络（CNN）受到越来越多的关注。然而，现有的基于CNN的轻量级网络和注意力机制在特征提取和增强方面仍然存在冗余。本文提出了用于轻量级超分辨率的高效自校准和分层细化网络（ESHR），其中包括三种有效的设计。首先，我们提出了一种更有效的特征提取基础模块，称为自校准残差块（SRB），它在替换传统卷积、BSConv 和 DSConv 等其他常见卷积模块时可以获得优越的性能。其次，我们提出了一种更轻量级的空间注意力机制，称为挤压激励空间注意力（SESA），它可以在空间维度上更有效地交互和增强特征，以提高模型的表示能力。第三，我们设计了一个高效的分层细化块（EHRB）来集成和利用多级特征，在不增加参数数量的情况下可以获得比单级细化更好的性能。大量实验表明，所提出的方法在数量和质量上都优于最先进的基于 CNN 的方法。