To achieve restoration of high frequency information for an undersampled and degraded low-resolution image, a nonlinear and real-time processing method-the radial basis function (RBF) neural network based super-resolu...To achieve restoration of high frequency information for an undersampled and degraded low-resolution image, a nonlinear and real-time processing method-the radial basis function (RBF) neural network based super-resolution method of restoration is proposed. The RBF network configuration and processing method is suitable for a high resolution restoration from an undersampled low-resolution image. The soft-competition learning scheme based on the k-means algorithm is used, and can achieve higher mapping approximation accuracy without increase in the network size. Experiments showed that the proposed algorithm can achieve a super-resolution restored image from an undersampled and degraded low-resolution image, and requires a shorter training time when compared with the multiplayer perception (MLP) network.展开更多
A new method of super-resolution image reconstruction is proposed, which uses a three-step-training error backpropagation neural network （BPNN） to realize the super-resolution reconstruction （SRR） of satellite ima...A new method of super-resolution image reconstruction is proposed, which uses a three-step-training error backpropagation neural network （BPNN） to realize the super-resolution reconstruction （SRR） of satellite image. The method is based on BPNN. First, three groups learning samples with different resolutions are obtained according to image observation model, and then vector mappings are respectively used to those three group learning samples to speed up the convergence of BPNN, at last, three times consecutive training are carried on the BPNN. Training samples used in each step are of higher resolution than those used in the previous steps, so the increasing weights store a great amount of information for SRR, and network performance and generalization ability are improved greatly. Simulation and generalization tests are carried on the well-trained three-step-training NN respectively, and the reconstruction results with higher resolution images verify the effectiveness and validity of this method.展开更多
The bootstrap resampling method is applied to an ensemble artificial neural network (ANN) approach (which combines machine learning with physical data obtained from a numerical weather prediction model) to provide a m...The bootstrap resampling method is applied to an ensemble artificial neural network (ANN) approach (which combines machine learning with physical data obtained from a numerical weather prediction model) to provide a multi-ANN model super-ensemble for application to multi-step-ahead forecasting of wind speed and of the associated power generated from a wind turbine. A statistical combination of the individual forecasts from the various ANNs of the super-ensemble is used to construct the best deterministic forecast, as well as the prediction uncertainty interval associated with this forecast. The bootstrapped neural-network methodology is validated using measured wind speed and power data acquired from a wind turbine in an operational wind farm located in northern China.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
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
This paper proposes a two-stage point cloud super resolution framework that combines local interpolation and deep neural network based readjustment. For the first stage, the authors apply a local interpolation method ...This paper proposes a two-stage point cloud super resolution framework that combines local interpolation and deep neural network based readjustment. For the first stage, the authors apply a local interpolation method to increase the density and uniformity of the target point cloud. For the second stage, the authors employ an outer-product neural network to readjust the position of points that are inserted at the first stage. Comparison examples are given to demonstrate that the proposed framework achieves a better accuracy than existing state-of-art approaches, such as PU-Net, Point Net and DGCNN(Source code is available at https://github.com/qwerty1319/PC-SR).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
文摘To achieve restoration of high frequency information for an undersampled and degraded low-resolution image, a nonlinear and real-time processing method-the radial basis function (RBF) neural network based super-resolution method of restoration is proposed. The RBF network configuration and processing method is suitable for a high resolution restoration from an undersampled low-resolution image. The soft-competition learning scheme based on the k-means algorithm is used, and can achieve higher mapping approximation accuracy without increase in the network size. Experiments showed that the proposed algorithm can achieve a super-resolution restored image from an undersampled and degraded low-resolution image, and requires a shorter training time when compared with the multiplayer perception (MLP) network.
文摘A new method of super-resolution image reconstruction is proposed, which uses a three-step-training error backpropagation neural network （BPNN） to realize the super-resolution reconstruction （SRR） of satellite image. The method is based on BPNN. First, three groups learning samples with different resolutions are obtained according to image observation model, and then vector mappings are respectively used to those three group learning samples to speed up the convergence of BPNN, at last, three times consecutive training are carried on the BPNN. Training samples used in each step are of higher resolution than those used in the previous steps, so the increasing weights store a great amount of information for SRR, and network performance and generalization ability are improved greatly. Simulation and generalization tests are carried on the well-trained three-step-training NN respectively, and the reconstruction results with higher resolution images verify the effectiveness and validity of this method.
文摘The bootstrap resampling method is applied to an ensemble artificial neural network (ANN) approach (which combines machine learning with physical data obtained from a numerical weather prediction model) to provide a multi-ANN model super-ensemble for application to multi-step-ahead forecasting of wind speed and of the associated power generated from a wind turbine. A statistical combination of the individual forecasts from the various ANNs of the super-ensemble is used to construct the best deterministic forecast, as well as the prediction uncertainty interval associated with this forecast. The bootstrapped neural-network methodology is validated using measured wind speed and power data acquired from a wind turbine in an operational wind farm located in northern China.
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.
基金supported by the NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization under Grant No.U1909210the National Nature Science Foundation of China under Grant Nos.61761136010,61772163。
文摘This paper proposes a two-stage point cloud super resolution framework that combines local interpolation and deep neural network based readjustment. For the first stage, the authors apply a local interpolation method to increase the density and uniformity of the target point cloud. For the second stage, the authors employ an outer-product neural network to readjust the position of points that are inserted at the first stage. Comparison examples are given to demonstrate that the proposed framework achieves a better accuracy than existing state-of-art approaches, such as PU-Net, Point Net and DGCNN(Source code is available at https://github.com/qwerty1319/PC-SR).
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。
