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作 者:程玉 郑华 陈晓文[1] 林烁烁[1] 张明伟 CHENG Yu;ZHENG Hua;CHEN Xiao-Wen;LIN Shuo-Shuo;ZHANG Ming-Wei(College of Photonic and Electronic Engineering,Fujian Normal University,Fuzhou 350007,China;Key Laboratory of Optoelectronic Science and Technology for Medicine(Ministry of Education),Fujian Normal University,Fuzhou 350007,China;Fujian Provincial Key Laboratory of Photonics Technology,Fujian Normal University,Fuzhou 350007,China;Fujian Provincial Engineering Research Center for Optoelectronic Sensors and Intelligent Information,Fuzhou 350007,China)
机构地区:[1]福建师范大学光电与信息工程学院,福州350007 [2]福建师范大学医学光电科学与技术教育部重点实验室,福州350007 [3]福建师范大学福建省光子技术重点实验室,福州350007 [4]福建师范大学福建省光电传感应用工程技术研究中心,福州350007
出 处:《计算机系统应用》2021年第1期135-140,共6页Computer Systems & Applications
基 金:福建省自然科学基金(2017J01744)。
摘 要:近年来,随着科学技术的高速发展,深度学习的蓬勃兴起,实现图像超分辨率重建成为计算机视觉领域一大热门研究课题.然而网络深度增加容易引起训练困难,并且网络无法获取准确的高频信息,导致图像重建效果差.本文提出基于密集残差注意力网络的图像超分辨率算法来解决这些问题.该算法主要采用密集残差网络,在加快模型收敛速度的同时,减轻了梯度消失问题.注意力机制的加入,使网络高频有效信息较大的权重,减少模型计算成本.实验证明,基于密集残差注意力网络的图像超分辨率算法在模型收敛速度上极大地提升,图像细节恢复效果令人满意.In recent years,with the rapid development of science and technology and the rise of deep learning,achieving image super-resolution reconstruction has become a hot research topic in the field of computer vision.However,the increase in network depth is easy to cause training difficulties,and the network cannot obtain accurate high-frequency information,resulting in poor image reconstruction.This study proposes an image super-resolution algorithm based on residual dense attention network to solve these problems.The algorithm mainly uses residual dense network,which accelerates the model convergence speed and reduces the gradient vanishing problem.The addition of attention mechanism makes the high-frequency effective information of the network have a larger weight and reduces the model calculation cost.Experiments show that the image super-resolution algorithm based on residual dense attention network greatly improves the model convergence speed,and the image detail recovery effect is satisfactory.
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