Bai Haomou, Liang Xiao
College of Computer and Communication, Lanzhou University of Technology, Gansu, 730050, China.
Sci Rep. 2024 Jun 15;14(1):13850. doi: 10.1038/s41598-024-64724-y.
Recently, ConvNeXt and blueprint separable convolution (BSConv) constructed from standard ConvNet modules have demonstrated competitive performance in advanced computer vision tasks. This paper proposes an efficient model (BCRN) based on BSConv and the ConvNeXt residual structure for single image super-resolution, which achieves superior performance with very low parametric numbers. Specifically, the residual block (BCB) of the BCRN utilizes the ConvNeXt residual structure and BSConv to significantly reduce the number of parameters. Within the residual block, enhanced spatial attention and contrast-aware channel attention modules are simultaneously introduced to prioritize valuable features within the network. Multiple residual blocks are then stacked to form the backbone network, with Dense connections utilized between them to enhance feature utilization. Our model boasts extremely low parameters compared to other state-of-the-art lightweight models, while experimental results on benchmark datasets demonstrate its excellent performance. The code will be available at https://github.com/kptx666/BCRN .
最近，由标准卷积神经网络（ConvNet）模块构建的ConvNeXt和蓝图可分离卷积（BSConv）在先进的计算机视觉任务中展现出了具有竞争力的性能。本文提出了一种基于BSConv和ConvNeXt残差结构的高效单图像超分辨率模型（BCRN），该模型在参数数量非常少的情况下实现了卓越的性能。具体而言，BCRN的残差块（BCB）利用ConvNeXt残差结构和BSConv显著减少了参数数量。在残差块内，同时引入了增强空间注意力和对比度感知通道注意力模块，以在网络中优先处理有价值的特征。然后堆叠多个残差块以形成骨干网络，并在它们之间使用密集连接来提高特征利用率。与其他当前最先进的轻量级模型相比，我们的模型拥有极低的参数，而在基准数据集上的实验结果证明了其优异的性能。代码将在https://github.com/kptx666/BCRN上提供。
