论文标题
蓝图可分离剩余网络，用于有效图像超分辨率
Blueprint Separable Residual Network for Efficient Image Super-Resolution
论文作者
论文摘要
单图超分辨率（SISR）的最新进展已取得了非凡的性能，但是计算成本太重，无法应用于边缘设备。为了减轻这个问题，已经提出了许多新颖有效的解决方案。卷积神经网络（CNN）具有注意机制，由于其效率和有效性引起了人们的关注。但是，卷积操作仍然存在冗余。在本文中，我们提出了包含两个有效设计的蓝图可分离剩余网络（BSRN）。一种是用于冗余卷积操作发生的蓝图可分离卷积（BSCONV）。另一个是通过引入更有效的注意模块来增强模型能力。实验结果表明，BSRN在现有有效的SR方法中实现了最新的性能。此外，我们模型BSRN-S的较小变体赢得了NTIRE 2022高效SR挑战的模型复杂性轨迹的第一名。该代码可在https://github.com/xiaom233/bsrn上找到。
Recent advances in single image super-resolution (SISR) have achieved extraordinary performance, but the computational cost is too heavy to apply in edge devices. To alleviate this problem, many novel and effective solutions have been proposed. Convolutional neural network (CNN) with the attention mechanism has attracted increasing attention due to its efficiency and effectiveness. However, there is still redundancy in the convolution operation. In this paper, we propose Blueprint Separable Residual Network (BSRN) containing two efficient designs. One is the usage of blueprint separable convolution (BSConv), which takes place of the redundant convolution operation. The other is to enhance the model ability by introducing more effective attention modules. The experimental results show that BSRN achieves state-of-the-art performance among existing efficient SR methods. Moreover, a smaller variant of our model BSRN-S won the first place in model complexity track of NTIRE 2022 Efficient SR Challenge. The code is available at https://github.com/xiaom233/BSRN.
