Efficient deep learning-based approaches have achieved remarkable performance in single image super-resolution. However, recent studies on efficient super-resolution have mainly focused on reducing the number of parameters and floating-point operations through various network designs. Although these methods can decrease the number of parameters and floating-point operations, they may not necessarily reduce actual running time. To address this issue, we propose a novel multi-stage lightweight network boosting method, which can enable lightweight networks to achieve outstanding performance. Specifically, we leverage enhanced high-resolution output as additional supervision to improve the learning ability of lightweight student networks. Upon convergence of the student network, we further simplify our network structure to a more lightweight level using reparameterization techniques and iterative network pruning. Meanwhile, we adopt an effective lightweight network training strategy that combines multi-anchor distillation and progressive learning, enabling the lightweight network to achieve outstanding performance. Ultimately, our proposed method achieves the fastest inference time among all participants in the NTIRE 2023 efficient super-resolution challenge while maintaining competitive super-resolution performance. Additionally, extensive experiments are conducted to demonstrate the effectiveness of the proposed components. The results show that our approach achieves comparable performance in representative dataset DIV2K, both qualitatively and quantitatively, with faster inference and fewer number of network parameters.

翻译：基于深度学习的轻量化方法已在单图像超分辨率任务中取得显著性能。然而，近期高效超分辨率研究主要聚焦于通过多样化网络设计减少参数量和浮点运算次数。尽管这些方法能降低参数量和浮点运算量，但未必能缩短实际运行时间。为解决该问题，我们提出一种新颖的多阶段轻量网络增强方法，可使轻量网络实现卓越性能。具体而言，我们利用增强的高分辨率输出作为额外监督信号，提升轻量学生网络的学习能力。当学生网络收敛后，通过重参数化技术与迭代网络剪枝，进一步将网络结构精简至更轻量级。同时，我们采用结合多锚点蒸馏与渐进式学习的有效轻量网络训练策略，使轻量网络达到出色性能。最终，所提方法在NTIRE 2023高效超分辨率挑战赛所有参赛方案中实现了最快推理时间，同时保持具有竞争力的超分辨率性能。此外，通过大量实验验证了各设计组件的有效性。结果表明，本方法在代表性数据集DIV2K上，无论定性还是定量评估均取得可比性能，且推理速度更快、网络参数量更少。