高效二阶注意力对偶回归网络的超分辨率重建

doi:10.3778/j.issn.1002-8331.2103-0302

摘要/Abstract

摘要： 针对中间层通道特征相关性利用率低、低分辨率图像和高分辨率图像函数映射空间非线性的问题，提出了一种基于高效二阶注意力机制的对偶回归网络（ESADRNet）。该网络将重建任务分为两个回归网络：原始回归网络和对偶回归网络。原始回归网络采用FReLU为激活函数的下采样层对图像进行更高效的空间上下文特征提取；基于多级跳跃连接残差块（MLSCR）和高效二阶通道注意力模块（ESOCA）构成的多级跳跃连接残差注意力模块（MLSCRAG）、共享源跳跃连接（SSC）和亚像素卷积构建渐进式上采样网络，使网络专注于更具辨别性的特征表示，具有更强大的特征表达和特征相关学习能力；利用对偶回归网络约束映射空间，寻找最优重建函数。在Set5、Set14、BSD100和Urban109数据集上经过对比实验证明，该网络在客观定量指标和主观视觉方面均优于其他对比方法。

关键词: 超分辨率重建, 注意力机制, 对偶回归网络, 卷积神经网络, 深度学习

Abstract: Aiming at low channel feature correlation utilization in the middle layer and nonlinear mapping of low-resolution images and high-resolution images, a dual regression network based on an efficient second-order attention（ESADRNet） is proposed. The network divides the reconstruction task into two regression networks：the original regression network and the dual regression network. The original regression network firstly uses FReLU as the down-sampling layer of the activation function to extract more efficient spatial context features of the image. Meanwhile, the method based on the multi-level skip connection residual attention group（MLSCRAG） composed of the multi-level skip connection residual（MLSCR） and the efficient second-order channel attention（ESOCA） module and shared source connection（SSC） and sub-pixel convolution constructs a progressive up-sampling network, so that the network can focus on more discriminative feature representation, and has more powerful feature expression and feature-related learning capabilities. Finally, the dual regression network is used to constrain the mapping space, and find the optimal reconstruction function. After comparative experiments on the Set5, Set14, BSD100 and Urban109 datasets, it is proved that the network is superior to other methods in terms of objective quantitative indicators and subjective vision.

Key words: super-resolution reconstruction, attention mechanism, dual regression network, convolutional neural network, deep learning

廉炜雯, 吴斌, 张红英, 李雪. 高效二阶注意力对偶回归网络的超分辨率重建[J]. 计算机工程与应用, 2022, 58(20): 220-228.

LIAN Weiwen, WU Bin, ZHANG Hongying, LI Xue. Super-Resolution Reconstruction of Efficient Second-Order Attention Dual Regression Network[J]. Computer Engineering and Applications, 2022, 58(20): 220-228.

参考文献

[1] TIAN J，MA K.A survey on super-resolution imaging[J].Signal，Image and Video Processing，2011，5（3）：329-342.
[2] 刘可文，马圆，熊红霞，等.基于残差通道注意力网络的医学图像超分辨率重建方法[J].激光与光电子学进展，2020，57（2）：161-169.
LIU Kewen，MA Yuan，XIONG Hongxia，et al.Medical-image super-resolution reconstruction method based on residual channel attention network[J].Laser & Optoelectronics Progress，2020，57（2）：161-169.
[3] KEYS R G.Cubic convolution interpolation for digital image processing[J].IEEE Transactions on Acoustics Speech and Signal Processing，1982，29（6）：1153-1160.
[4] 谢海平，谢凯利，杨海涛.图像超分辨率方法研究进展[J].计算机工程与应用，2020，56（19）：34-41.
XIE Haiping，XIE Kaili，YANG Haitao.Research progress of image super-resolution methods[J].Computer Engineering and Applications，2020，56（19）：34-41.
[5] PROTTER M，ELAD M，TAKEDA H，et al.Generalizing the nonlocal-means to super-resolution reconstruction[J].IEEE Transactions on Image Processing，2008，18（1）：36-51.
[6] HUANG J J，SIU W C，LIU T R.Fast image interpolation via random forests[J].IEEE Transactions on Image Processing，2015，24（10）：3232-3245.
[7] 曾凯，丁世飞.图像超分辨率重建的研究进展[J].计算机工程与应用，2017，53（16）：29-35.
ZENG Kai，DING Shifei.Advances in image super-resolution reconstruction[J].Computer Engineering and Applications，2017，53（16）：29-35.
[8] 陈晨，刘明明，刘兵，等.基于残差网络的图像超分辨率重建算法[J].计算机工程与应用，2020，56（8）：185-191.
CHEN Chen，LIU Mingming，LIU Bing，et al.Image super-resolution reconstruction algorithm based on residual network[J].Computer Engineering and Applications，2020，56（8）：185-191.
[9] 彭晏飞，高艺，杜婷婷，等.生成对抗网络的单图像超分辨率重建方法[J].计算机科学与探索，2020，14（9）：1612-1620.
PENG Yanfei，GAO Yi，DU Tingting，et al.Single image super-resolution reconstruction method for generative adversarial network[J].Journal of Frontiers of Computer Science and Technology，2020，14（9）：1612-1620.
[10] DONG C，LOY C C，HE K，et al.Image super-resolution using deep convolutional networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2014，38（2）：295-307.
[11] DONG C，LOY C C，TANG X.Accelerating the Super-Resolution Convolutional Neural Network[J].Lecture Notes in Computer Science（Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics），2016，9906：391-407.
[12] KIM J，LEE J，LEE K.Accurate image super-resolution using very deep convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：1646-1654.
[13] KIM J，LEE J K，LEE K M.Deeply-recursive convolutional network for image super-resolution[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2016：1637-1645.
[14] LI Z，YANG J，LIU Z，et al.Feedback network for image super-resolution[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2019：3867-3876.
[15] ZHU S，CAO R，YU K.Dual learning for semi-supervised natural language understanding[J].IEEE/ACM Transactions on Audio，Speech，and Language Processing，2020，28：1936-1947.
[16] HE D，XIA Y，QIN T，et al.Dual learning for machine translation[C]//Advances in Neural Information Processing Systems，2016：820-828.
[17] MOHRI M，ROSTAMIZADEH A，TALWALKAR A.Foundations of machine learning[M].2nd ed.[S.l.]：MIT Press，2018.
[18] WANG Q，WU B，ZHU P，et al.ECA-Net：efficient channel attention for deep convolutional neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2020：11534-11542.
[19] HU J，SHEN L，SUN G.Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition（CVPR），2018：7132-7141.
[20] RONNEBERGER O，FISCHER P，BROX T.U-Net：convolutional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention，2015：234-241.
[21] MA N，ZHANG X，SUN J.Funnel activation for visual recognition[C]//European Conference on Computer Vision，2020.
[22] SHI W，CABALLERO J，HUSZáR F，et al.Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]//IEEE Conference on Computer Vision and Pattern Recognition，2016：1874-1883.