摘要
受部分容积效应影响,土壤计算机断层扫描(Computed Tomography,CT)图像存在孔隙边界模糊现象,影响土壤孔隙结构研究的准确性。针对该问题,该研究提出基于序列信息的生成式对抗网络(Sequence information Generative Adversarial Network,SeqGAN),实现土壤CT图像的超分辨率重建。针对土壤CT序列图像具有较高相似性的特点,SeqGAN法引入序列卷积块挖掘前后图像的序列信息,并将多重特征增强融合于目标图像中;利用多层残差块提取图像特征,构建残差块输入和输出的直接连接,以减少模型退化;利用对抗网络实现损失间接反馈,提高模型的特征学习能力。在序列相似性较高的土壤图像数据集验证了该方法性能。结果表明,SeqGAN法均方误差比次优方法GAN降低25%,峰值信噪比提升1.4 dB,结构相似性提升0.2%。重建的土壤图像具有较高准确率和清晰度,可为后续土壤物理学研究提供准确的数据基础。
Pore boundary is generally blur resulted from the partial volume in the soil CT image.This phenomenon has inevitably posed a great influence on the accuracy of soil pore topology.This study aims to propose a novel Sequence information Generative Adversarial Network(SeqGAN)to realize the Super-Resolution reconstruction of soil CT images.Therefore,the SeqGAN was selected to improve the clarity and accuracy of soil CT images,particularly for the high resolution and feature boundaries.Two improvements of SeqGAN were utilized,including the Sequential Convolution block(SeqConv)structure,and Beginning-to-End Residuals Connection block(BE-Resblock).SeqConv structure involved two convolution block structures.The first convolution block was used to extract the feature of the target image,while the second was used to extract the sequence information of the next and previous image in the sequence,thereby realizing the extraction of sequence information.In the BE-Resblock,more than 8 residual blocks were connected in series to extract the image information.At the same time,the residual blocks of the beginning and end were also connected,where the input information was introduced to reduce the probability of overfitting.Furthermore,twice up-sampling blocks were used to improve the resolution of images,where the final output was a 4x high-definition Super-Resolution image.The experimental samples soil was taken from Keshan Farm in the northwest of Keshan County,Qiqihar City,Heilongjiang Province(125°23′57″E,48°18′37″N).Soil samples were collected with a cutting ring and stored in a plexiglass tube.A submerging test was also conducted to obtain the soil samples.A spiral CT scanner was then used to capture soil CT images.The test datasets were finally taken as the 440 soil CT sequence images with high sequence Structural Similarity(SSIM).Two datasets were obtained after preprocessed,including the high-and low-resolution images with twice the difference in resolution.Specifically,the low-resolution image dataset containe
作者
韩巧玲
周希博
宋润泽
赵玥
Han Qiaoling;Zhou Xibo;Song Runze;Zhao Yue(School of Technology,Beijing Forestry University,Beijing 100083,China;Key Lab of State Forestry Administration for Forestry Equipment and Automation,Beijing 100083,China;Beijing Laboratory of Urban and Rural Ecological Environment,Beijing 100083,China;College of Information and Electrical Engineering,China Agricultural University,Beijing 100091,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2021年第17期90-96,共7页
Transactions of the Chinese Society of Agricultural Engineering
基金
中央高校基本科研业务费专项资金项目(BLX202017)
国家自然科学基金面上项目(32071838)
中国博士后科学基金(2020M680409)
北京市共建项目专项资助。
