In recent years, deep convolutional neural networks have shown superior performance in image denoising. However, deep network structures often come with a large number of model parameters, leading to high training cos...In recent years, deep convolutional neural networks have shown superior performance in image denoising. However, deep network structures often come with a large number of model parameters, leading to high training costs and long inference times, limiting their practical application in denoising tasks. This paper proposes a new dual convolutional denoising network with skip connections(DECDNet), which achieves an ideal balance between denoising effect and network complexity. The proposed DECDNet consists of a noise estimation network, a multi-scale feature extraction network, a dual convolutional neural network, and dual attention mechanisms. The noise estimation network is used to estimate the noise level map, and the multi-scale feature extraction network is combined to improve the model's flexibility in obtaining image features. The dual convolutional neural network branch design includes convolution and dilated convolution interactive connections, with the lower branch consisting of dilated convolution layers, and both branches using skip connections. Experiments show that compared with other models, the proposed DECDNet achieves superior PSNR and SSIM values at all compared noise levels, especially at higher noise levels, showing robustness to images with higher noise levels. It also demonstrates better visual effects, maintaining a balance between denoising and detail preservation.展开更多
Using premium casing connections instead of API ones is one of the mosteffective technique to prevent casing failure. The factors contribute to the strength of premiumcasing connections are studied with FEA and full-s...Using premium casing connections instead of API ones is one of the mosteffective technique to prevent casing failure. The factors contribute to the strength of premiumcasing connections are studied with FEA and full-scale test. The criterions are presented thatensure the connection's strength higher than the pipe. At the same time, the method is given todecrease the peak stress of the connection so as to improve its anticorruption property. At last,full-scale tests are done to test the strength of the connections designed with the methoddescribed, the results show that the connection's strength is higher than the pipe. This indicatedthat the method described is effective in designing premium casing connection.展开更多
目的甲状腺结节的精准分割在医学影像处理中具有重要意义,然而,超声图像中的结节通常具有尺寸多变和边缘模糊的特点,这为其准确分割带来了挑战。为有效应对这一挑战,本文提出了一种结合卷积神经网络(convolutional neural network,CNN)...目的甲状腺结节的精准分割在医学影像处理中具有重要意义,然而,超声图像中的结节通常具有尺寸多变和边缘模糊的特点,这为其准确分割带来了挑战。为有效应对这一挑战,本文提出了一种结合卷积神经网络(convolutional neural network,CNN)和Transformer的分割网络,命名为TransUNet,旨在实现对甲状腺结节超声图像的精准分割。方法首先,使用卷积神经网络对超声图像进行编码,以生成特征图。接着,将特征图转换为序列向量,并利用Transformer的编码机制来捕捉上下文信息。此外,为保持局部细节特征的完整性,研究组还引入了跳跃连接,将其用于在解码器中对编码特征进行上采样,这对于处理边缘模糊等问题尤为重要。结果通过在甲状腺结节图像分割任务中进行广泛的实验,验证TransUNet的有效性。具体而言,骰子系数(dice coefficient,DICE)为0.75,交并比(intersection over union,IoU)为0.60,F1分数(F1 Score)为0.72,准确率高达0.93,AUC(area under the ROC curve)为0.91。这些性能指标反映了该方法在处理尺寸多变和边缘模糊等挑战方面的出色表现。结论本文提出的TransUNet为甲状腺结节超声图像分割任务带来了显著的性能提升。相较于传统的U-Net方法,TransUNet不仅更好地处理了尺寸多变和边缘模糊等挑战,而且在分割性能上具有更为出色的表现,为医学图像处理领域的进一步研究和临床应用提供了有力支持。展开更多
基金funded by National Nature Science Foundation of China,grant number 61302188。
文摘In recent years, deep convolutional neural networks have shown superior performance in image denoising. However, deep network structures often come with a large number of model parameters, leading to high training costs and long inference times, limiting their practical application in denoising tasks. This paper proposes a new dual convolutional denoising network with skip connections(DECDNet), which achieves an ideal balance between denoising effect and network complexity. The proposed DECDNet consists of a noise estimation network, a multi-scale feature extraction network, a dual convolutional neural network, and dual attention mechanisms. The noise estimation network is used to estimate the noise level map, and the multi-scale feature extraction network is combined to improve the model's flexibility in obtaining image features. The dual convolutional neural network branch design includes convolution and dilated convolution interactive connections, with the lower branch consisting of dilated convolution layers, and both branches using skip connections. Experiments show that compared with other models, the proposed DECDNet achieves superior PSNR and SSIM values at all compared noise levels, especially at higher noise levels, showing robustness to images with higher noise levels. It also demonstrates better visual effects, maintaining a balance between denoising and detail preservation.
文摘Using premium casing connections instead of API ones is one of the mosteffective technique to prevent casing failure. The factors contribute to the strength of premiumcasing connections are studied with FEA and full-scale test. The criterions are presented thatensure the connection's strength higher than the pipe. At the same time, the method is given todecrease the peak stress of the connection so as to improve its anticorruption property. At last,full-scale tests are done to test the strength of the connections designed with the methoddescribed, the results show that the connection's strength is higher than the pipe. This indicatedthat the method described is effective in designing premium casing connection.
文摘目的甲状腺结节的精准分割在医学影像处理中具有重要意义,然而,超声图像中的结节通常具有尺寸多变和边缘模糊的特点,这为其准确分割带来了挑战。为有效应对这一挑战,本文提出了一种结合卷积神经网络(convolutional neural network,CNN)和Transformer的分割网络,命名为TransUNet,旨在实现对甲状腺结节超声图像的精准分割。方法首先,使用卷积神经网络对超声图像进行编码,以生成特征图。接着,将特征图转换为序列向量,并利用Transformer的编码机制来捕捉上下文信息。此外,为保持局部细节特征的完整性,研究组还引入了跳跃连接,将其用于在解码器中对编码特征进行上采样,这对于处理边缘模糊等问题尤为重要。结果通过在甲状腺结节图像分割任务中进行广泛的实验,验证TransUNet的有效性。具体而言,骰子系数(dice coefficient,DICE)为0.75,交并比(intersection over union,IoU)为0.60,F1分数(F1 Score)为0.72,准确率高达0.93,AUC(area under the ROC curve)为0.91。这些性能指标反映了该方法在处理尺寸多变和边缘模糊等挑战方面的出色表现。结论本文提出的TransUNet为甲状腺结节超声图像分割任务带来了显著的性能提升。相较于传统的U-Net方法,TransUNet不仅更好地处理了尺寸多变和边缘模糊等挑战,而且在分割性能上具有更为出色的表现,为医学图像处理领域的进一步研究和临床应用提供了有力支持。
