Limited by diffraction limit, low spatial resolution is one of the shortcomings of terahertz imaging. Low spatial resolution is also one of the reasons limiting the development of stress measurement using terahertz im...Limited by diffraction limit, low spatial resolution is one of the shortcomings of terahertz imaging. Low spatial resolution is also one of the reasons limiting the development of stress measurement using terahertz imaging. In this paper, the full-field stress measurement using Terahertz Time Domain Spectroscopy (THz-TDS) is combined with Super-Resolution Convolutional Neural Network (SRCNN) algorithm to obtain stress fields with high spatial resolution. A modulation model from a plane stress state to a THz-TDS signal is constructed. A large number of simulated sets are obtained to train the SRCNN model. By applying the trained SRCNN model to imaging the numerical and physical stress fields, the improved spatial resolution of stress field calculated from the captured THz-TDS signal is obtained.展开更多
Aiming at the problems of image super-resolution algorithm with many convolutional neural networks, such as large parameters, large computational complexity and blurred image texture, we propose a new algorithm model....Aiming at the problems of image super-resolution algorithm with many convolutional neural networks, such as large parameters, large computational complexity and blurred image texture, we propose a new algorithm model. The classical convolutional neural network is improved, the convolution kernel size is adjusted, and the parameters are reduced;the pooling layer is added to reduce the dimension. Reduced computational complexity, increased learning rate, and reduced training time. The iterative back-projection algorithm is combined with the convolutional neural network to create a new algorithm model. The experimental results show that compared with the traditional facial illusion method, the proposed method can obtain better performance.展开更多
Purpose: To detect small diagnostic signals such as lung nodules in chest radiographs, radiologists magnify a region-of-interest using linear interpolation methods. However, such methods tend to generate over-smoothed...Purpose: To detect small diagnostic signals such as lung nodules in chest radiographs, radiologists magnify a region-of-interest using linear interpolation methods. However, such methods tend to generate over-smoothed images with artifacts that can make interpretation difficult. The purpose of this study was to investigate the effectiveness of super-resolution methods for improving the image quality of magnified chest radiographs. Materials and Methods: A total of 247 chest X-rays were sampled from the JSRT database, then divided into 93 training cases with non-nodules and 154 test cases with lung nodules. We first trained two types of super-resolution methods, sparse-coding super-resolution (ScSR) and super-resolution convolutional neural network (SRCNN). With the trained super-resolution methods, the high-resolution image was then reconstructed using the super-resolution methods from a low-resolution image that was down-sampled from the original test image. We compared the image quality of the super-resolution methods and the linear interpolations (nearest neighbor and bilinear interpolations). For quantitative evaluation, we measured two image quality metrics: peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). For comparative evaluation of the super-resolution methods, we measured the computation time per image. Results: The PSNRs and SSIMs for the ScSR and the SRCNN schemes were significantly higher than those of the linear interpolation methods (p p p Conclusion: Super-resolution methods provide significantly better image quality than linear interpolation methods for magnified chest radiograph images. Of the two tested schemes, the SRCNN scheme processed the images fastest;thus, SRCNN could be clinically superior for processing radiographs in terms of both image quality and processing speed.展开更多
针对现有超分辨率重建网络具有较高的计算复杂度和存在大量内存消耗的问题,提出了一种基于Transformer-CNN的轻量级图像超分辨率重建网络,使超分辨率重建网络更适合应用于移动平台等嵌入式终端。首先,提出了一个基于Transformer-CNN的...针对现有超分辨率重建网络具有较高的计算复杂度和存在大量内存消耗的问题,提出了一种基于Transformer-CNN的轻量级图像超分辨率重建网络,使超分辨率重建网络更适合应用于移动平台等嵌入式终端。首先,提出了一个基于Transformer-CNN的混合模块,从而增强网络捕获局部−全局深度特征的能力;其次,提出了一个改进的倒置残差块来特别关注高频区域的特征,以提升特征提取能力和减少推理时间;最后,在探索激活函数的最佳选择后,采用GELU(Gaussian Error Linear Unit)激活函数来进一步提高网络性能。实验结果表明,所提网络可以在图像超分辨率性能和网络复杂度之间取得很好的平衡,而且在基准数据集Urban100上4倍超分辨率的推理速度达到91 frame/s,比优秀网络SwinIR(Image Restoration using Swin transformer)快11倍,表明所提网络能够高效地重建图像的纹理和细节,并减少大量的推理时间。展开更多
针对图像盲超分辨率网络计算参数多、模型庞大的问题,对快速且节省内存的轻量级图像非盲超分辨率网络(fast and memory-efficient image super resulotion network,FMEN)进行改进,提出了一种轻量级的快速且节省内存的图像盲超分辨率网络...针对图像盲超分辨率网络计算参数多、模型庞大的问题,对快速且节省内存的轻量级图像非盲超分辨率网络(fast and memory-efficient image super resulotion network,FMEN)进行改进,提出了一种轻量级的快速且节省内存的图像盲超分辨率网络(fast and memory-efficient image blind super resulotion network,FMEBN)。首先,通过图像退化模块模拟部分真实世界退化空间,使用退化预测模块预测低分辨率(low resolution,LR)图像的退化参数;然后,为能有效利用退化先验信息指导并约束网络进行重建,使用动态卷积对原网络特征提取、重建模块、高频注意力块(high frequency attention block,HFAB)结构进行改进;最后,使用生成对抗网络(generative adversarial network,GAN)对FMEN训练策略与损失函数进行优化,减小真实数据与生成数据的差异,生成更加真实、清晰的纹理、轮廓。实验结果表明,在合成图像数据集和真实图像数据集RealWorld-38上,该算法有较好的重建精度与视觉效果,模型大小12 MB,可以满足图像盲超分辨率网络的轻量级需求。展开更多
文摘Limited by diffraction limit, low spatial resolution is one of the shortcomings of terahertz imaging. Low spatial resolution is also one of the reasons limiting the development of stress measurement using terahertz imaging. In this paper, the full-field stress measurement using Terahertz Time Domain Spectroscopy (THz-TDS) is combined with Super-Resolution Convolutional Neural Network (SRCNN) algorithm to obtain stress fields with high spatial resolution. A modulation model from a plane stress state to a THz-TDS signal is constructed. A large number of simulated sets are obtained to train the SRCNN model. By applying the trained SRCNN model to imaging the numerical and physical stress fields, the improved spatial resolution of stress field calculated from the captured THz-TDS signal is obtained.
文摘Aiming at the problems of image super-resolution algorithm with many convolutional neural networks, such as large parameters, large computational complexity and blurred image texture, we propose a new algorithm model. The classical convolutional neural network is improved, the convolution kernel size is adjusted, and the parameters are reduced;the pooling layer is added to reduce the dimension. Reduced computational complexity, increased learning rate, and reduced training time. The iterative back-projection algorithm is combined with the convolutional neural network to create a new algorithm model. The experimental results show that compared with the traditional facial illusion method, the proposed method can obtain better performance.
文摘Purpose: To detect small diagnostic signals such as lung nodules in chest radiographs, radiologists magnify a region-of-interest using linear interpolation methods. However, such methods tend to generate over-smoothed images with artifacts that can make interpretation difficult. The purpose of this study was to investigate the effectiveness of super-resolution methods for improving the image quality of magnified chest radiographs. Materials and Methods: A total of 247 chest X-rays were sampled from the JSRT database, then divided into 93 training cases with non-nodules and 154 test cases with lung nodules. We first trained two types of super-resolution methods, sparse-coding super-resolution (ScSR) and super-resolution convolutional neural network (SRCNN). With the trained super-resolution methods, the high-resolution image was then reconstructed using the super-resolution methods from a low-resolution image that was down-sampled from the original test image. We compared the image quality of the super-resolution methods and the linear interpolations (nearest neighbor and bilinear interpolations). For quantitative evaluation, we measured two image quality metrics: peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). For comparative evaluation of the super-resolution methods, we measured the computation time per image. Results: The PSNRs and SSIMs for the ScSR and the SRCNN schemes were significantly higher than those of the linear interpolation methods (p p p Conclusion: Super-resolution methods provide significantly better image quality than linear interpolation methods for magnified chest radiograph images. Of the two tested schemes, the SRCNN scheme processed the images fastest;thus, SRCNN could be clinically superior for processing radiographs in terms of both image quality and processing speed.
文摘针对现有超分辨率重建网络具有较高的计算复杂度和存在大量内存消耗的问题,提出了一种基于Transformer-CNN的轻量级图像超分辨率重建网络,使超分辨率重建网络更适合应用于移动平台等嵌入式终端。首先,提出了一个基于Transformer-CNN的混合模块,从而增强网络捕获局部−全局深度特征的能力;其次,提出了一个改进的倒置残差块来特别关注高频区域的特征,以提升特征提取能力和减少推理时间;最后,在探索激活函数的最佳选择后,采用GELU(Gaussian Error Linear Unit)激活函数来进一步提高网络性能。实验结果表明,所提网络可以在图像超分辨率性能和网络复杂度之间取得很好的平衡,而且在基准数据集Urban100上4倍超分辨率的推理速度达到91 frame/s,比优秀网络SwinIR(Image Restoration using Swin transformer)快11倍,表明所提网络能够高效地重建图像的纹理和细节,并减少大量的推理时间。
文摘针对图像盲超分辨率网络计算参数多、模型庞大的问题,对快速且节省内存的轻量级图像非盲超分辨率网络(fast and memory-efficient image super resulotion network,FMEN)进行改进,提出了一种轻量级的快速且节省内存的图像盲超分辨率网络(fast and memory-efficient image blind super resulotion network,FMEBN)。首先,通过图像退化模块模拟部分真实世界退化空间,使用退化预测模块预测低分辨率(low resolution,LR)图像的退化参数;然后,为能有效利用退化先验信息指导并约束网络进行重建,使用动态卷积对原网络特征提取、重建模块、高频注意力块(high frequency attention block,HFAB)结构进行改进;最后,使用生成对抗网络(generative adversarial network,GAN)对FMEN训练策略与损失函数进行优化,减小真实数据与生成数据的差异,生成更加真实、清晰的纹理、轮廓。实验结果表明,在合成图像数据集和真实图像数据集RealWorld-38上,该算法有较好的重建精度与视觉效果,模型大小12 MB,可以满足图像盲超分辨率网络的轻量级需求。
