基于损失提取反馈注意网络的图像超分辨率重建研究  

Research on Image Super-resolution Reconstruction Based on Loss Extraction Feedback Attention Network

作　　者：孙红[1] 张玉香 凌岳览 Sun Hong;Zhang Yuxiang;Ling Yuelan(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《系统仿真学报》2023年第2期308-317,共10页Journal of System Simulation

基　　金：国家自然科学基金(61472256,61170277,61703277);沪江基金(C14002)。

摘　　要：自SRCNN(super-resolution convolutional neural network)将卷积神经网络用于超分辨率图像重建领域以来,人们通过大量的研究证明了使用深度学习的方法能够提高重建图像的效果。针对图像超分辨率网络中参数过多以及图像特征利用不充分导致可用的高频信息较少等问题,提出了一种基于损失提取策略的反馈注意网络(loss extraction feedback attention network,LEFAN),以循环的方式对参数进行复用,同时增加对低分辨率图像特征的重用,以捕获更多的高频信息,对重建过程中造成的损失进行提取并融合到最终的超分辨率图像中。实验结果表明:算法在实现多次利用低分辨率图像的基础上,对潜在的损失进行提取并融合到最终的超分辨率图像中,可以获得较好的图像重建效果。Since the first application of convolutional neural network to the field of super-resolution image reconstruction(super-resolution convolutional neural network, SRCNN), a large number of studies have proved that deep learning can improve the effect of image reconstruction. Aiming at the too many parameters in the image super-resolution network and the insufficient utilization of image features resulting in less available high-frequency information, a loss extraction feedback attention network(LEFAN) is proposed to reuse parameters in a circular way and increase the reuse of low-resolution image features to capture more high-frequency information. The loss caused in the reconstruction process is extracted and fused into the final super-resolution image. The experimental results show that the algorithm can obtain a better image reconstruction effect by extracting the potential loss and fusing it into the final super-resolution image on the basis of the multiple utilization of low-resolution images.

关 键 词：反馈机制 注意力机制 损失提取 超分辨率图像重建 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]