Single image super-resolution attempts to reconstruct a high-resolution (HR) image from its corresponding low-resolution (LR) image, which has been a research hotspot in computer vision and image processing for decades. To improve the accuracy of super-resolution images, many works adopt very deep networks to model the translation from LR to HR, resulting in memory and computation consumption. In this article, we design a lightweight dense connection distillation network by combining the feature fusion units and dense connection distillation blocks (DCDB) that include selective cascading and dense distillation components. The dense connections are used between and within the distillation block, which can provide rich information for image reconstruction by fusing shallow and deep features. In each DCDB, the dense distillation module concatenates the remaining feature maps of all previous layers to extract useful information, the selected features are then assessed by the proposed layer contrast-aware channel attention mechanism, and finally the cascade module aggregates the features. The distillation mechanism helps to reduce training parameters and improve training efficiency, and the layer contrast-aware channel attention further improves the performance of model. The quality and quantity experimental results on several benchmark datasets show the proposed method performs better tradeoff in term of accuracy and efficiency.

中文翻译：

---

具有密集连接蒸馏网络的轻量级单图像超分辨率

单图像超分辨率尝试从其对应的低分辨率（LR）图像重建高分辨率（HR）图像，这几十年来一直是计算机视觉和图像处理的研究热点。为了提高超分辨率图像的准确性，许多工作采用非常深的网络来对从 LR 到 HR 的转换进行建模，从而导致内存和计算消耗。在本文中，我们通过结合特征融合单元和密集连接蒸馏块（DCDB）设计了一个轻量级密集连接蒸馏网络，其中包括选择性级联和密集蒸馏组件。蒸馏块之间和内部使用密集连接，通过融合浅层和深层特征，可以为图像重建提供丰富的信息。在每个 DCDB 中，密集蒸馏模块连接所有先前层的剩余特征图以提取有用信息，然后通过提出的层对比度感知通道注意机制评估所选特征，最后级联模块聚合特征。蒸馏机制有助于减少训练参数并提高训练效率，而layer contrast-aware channel attention进一步提高了模型的性能。在几个基准数据集上的质量和数量实验结果表明，所提出的方法在准确性和效率方面表现更好。最后级联模块聚合特征。蒸馏机制有助于减少训练参数并提高训练效率，而layer contrast-aware channel attention进一步提高了模型的性能。在几个基准数据集上的质量和数量实验结果表明，所提出的方法在准确性和效率方面表现更好。最后级联模块聚合特征。蒸馏机制有助于减少训练参数并提高训练效率，而layer contrast-aware channel attention进一步提高了模型的性能。在几个基准数据集上的质量和数量实验结果表明，所提出的方法在准确性和效率方面表现更好。