The laser device is the core component of coherent Doppler wind lidar.The peak power and pulse width of laser transmitting pulse have important effects on SNR.Based on coherent Doppler wind pulse lidar,the peak power ...The laser device is the core component of coherent Doppler wind lidar.The peak power and pulse width of laser transmitting pulse have important effects on SNR.Based on coherent Doppler wind pulse lidar,the peak power and pulse width influence on SNR is studied on the theoretical derivation and analysis,and the results show that the higher the peak power can realize the greater the signal-to-noise ratio of coherent Doppler wind lidar.But when the peak power is too large,the laser pulse may appear nonlinear phenomenon,which cause the damage of the laser.So,the peak power must be less than the stimulated brillouin scattering power threshold.Increasing the pulse width can make the laser device to output more energy,but it will also make the spatial resolution lower,and the influence of turbulence on SNR will be greater.After a series of simulation analyses,it can be concluded that when the peak power is 650W and the pulse width is 340ns,the SNR of the system can be maximized.In addition,the coherent Doppler wind lidar system is set up to carry out corresponding experimental verification.The experimental results are consistent with the theoretical analysis and simulation,which verifies the correctness of the theoretical analysis and simulation results.It provides theoretical basis and practical ex-perience for the design of laser transmitting pulse in coherent Doppler wind lidar system.展开更多
The existence of shadow leads to the degradation of the image qualities and the defect of ground object information.Shadow removal is therefore an essential research topic in image processing filed.The biggest challen...The existence of shadow leads to the degradation of the image qualities and the defect of ground object information.Shadow removal is therefore an essential research topic in image processing filed.The biggest challenge of shadow removal is how to restore the content of shadow areas correctly while removing the shadow in the image.Paired regions for shadow removal approach based on multi-features is proposed, in which shadow removal is only performed on related sunlit areas.Feature distance between regions is calculated to find the optimal paired regions with considering of multi-features(texture, gradient feature, etc.) comprehensively.Images in different scenes with peak signal-to-noise ratio(PSNR) and structural similarity(SSIM) evaluation indexes are chosen for experiments.The results are shown with six existing comparison methods by visual and quantitative assessments, which verified that the proposed method shows excellent shadow removal effect, the brightness, color of the removed shadow area, and the surrounding non-shadow area can be naturally fused.展开更多
In recent years,enhancement of underwater images is a challenging task,which is gaining priority since the human eye cannot perceive images under water.The significant details underwater are not clearly captured using...In recent years,enhancement of underwater images is a challenging task,which is gaining priority since the human eye cannot perceive images under water.The significant details underwater are not clearly captured using the conventional image acquisition techniques,and also they are expensive.Hence,the quality of the image processing algorithms can be enhanced in the absence of costly and reliable acquisition techniques.Traditional algorithms have certain limitations in the case of these images with varying degrees of fuzziness and color deviation.In the proposed model,the authors used a deep learning model for underwater image enhancement.First,the original image is pre-processed by the white balance algorithm for colour correction and the contrast of the image is improved using the contrast enhancement technique.Next,the pre-processed image is given to the MIRNet for enhancement.MIRNet is a deep learning framework that can be used to enhance the low-light level images.The enhanced image quality is measured using peak signal-to-noise ratio(PSNR),root mean square error(RMSE),and structural similarity index(SSIM)parameters.展开更多
提出一种适用于去除高密度椒盐噪声的图像滤波算法,以进一步提高输出图像的峰值信噪比。利用直方图形状判定椒盐噪声的两种灰度值,用于噪声像素的检测与定位。对于非噪声像素,直接输出灰度值;对于噪声像素,沿其邻域的k个方向分别搜索一...提出一种适用于去除高密度椒盐噪声的图像滤波算法,以进一步提高输出图像的峰值信噪比。利用直方图形状判定椒盐噪声的两种灰度值,用于噪声像素的检测与定位。对于非噪声像素,直接输出灰度值;对于噪声像素,沿其邻域的k个方向分别搜索一个距离最近的非噪声像素,然后以欧式距离倒数为权重,采用k个非噪声像素的加权灰度均值作为噪声像素的输出灰度值。测试了不同的方向数k对滤波性能的影响,确定了k的最佳取值为4。采用该方法对椒盐噪声密度为10%到90%的图像进行滤波,输出图像的峰值信噪比比现有同类方法提高了1.8~4.7 d B。该方法有效提高了高密度椒盐噪声图像的滤波质量,处理速度满足实时要求。展开更多
文摘The laser device is the core component of coherent Doppler wind lidar.The peak power and pulse width of laser transmitting pulse have important effects on SNR.Based on coherent Doppler wind pulse lidar,the peak power and pulse width influence on SNR is studied on the theoretical derivation and analysis,and the results show that the higher the peak power can realize the greater the signal-to-noise ratio of coherent Doppler wind lidar.But when the peak power is too large,the laser pulse may appear nonlinear phenomenon,which cause the damage of the laser.So,the peak power must be less than the stimulated brillouin scattering power threshold.Increasing the pulse width can make the laser device to output more energy,but it will also make the spatial resolution lower,and the influence of turbulence on SNR will be greater.After a series of simulation analyses,it can be concluded that when the peak power is 650W and the pulse width is 340ns,the SNR of the system can be maximized.In addition,the coherent Doppler wind lidar system is set up to carry out corresponding experimental verification.The experimental results are consistent with the theoretical analysis and simulation,which verifies the correctness of the theoretical analysis and simulation results.It provides theoretical basis and practical ex-perience for the design of laser transmitting pulse in coherent Doppler wind lidar system.
基金Supported by the National Natural Science Foundation of China (No. 41971356, 41701446)the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural Resources (No. KF-2022-07-001)。
文摘The existence of shadow leads to the degradation of the image qualities and the defect of ground object information.Shadow removal is therefore an essential research topic in image processing filed.The biggest challenge of shadow removal is how to restore the content of shadow areas correctly while removing the shadow in the image.Paired regions for shadow removal approach based on multi-features is proposed, in which shadow removal is only performed on related sunlit areas.Feature distance between regions is calculated to find the optimal paired regions with considering of multi-features(texture, gradient feature, etc.) comprehensively.Images in different scenes with peak signal-to-noise ratio(PSNR) and structural similarity(SSIM) evaluation indexes are chosen for experiments.The results are shown with six existing comparison methods by visual and quantitative assessments, which verified that the proposed method shows excellent shadow removal effect, the brightness, color of the removed shadow area, and the surrounding non-shadow area can be naturally fused.
文摘In recent years,enhancement of underwater images is a challenging task,which is gaining priority since the human eye cannot perceive images under water.The significant details underwater are not clearly captured using the conventional image acquisition techniques,and also they are expensive.Hence,the quality of the image processing algorithms can be enhanced in the absence of costly and reliable acquisition techniques.Traditional algorithms have certain limitations in the case of these images with varying degrees of fuzziness and color deviation.In the proposed model,the authors used a deep learning model for underwater image enhancement.First,the original image is pre-processed by the white balance algorithm for colour correction and the contrast of the image is improved using the contrast enhancement technique.Next,the pre-processed image is given to the MIRNet for enhancement.MIRNet is a deep learning framework that can be used to enhance the low-light level images.The enhanced image quality is measured using peak signal-to-noise ratio(PSNR),root mean square error(RMSE),and structural similarity index(SSIM)parameters.
文摘提出一种适用于去除高密度椒盐噪声的图像滤波算法,以进一步提高输出图像的峰值信噪比。利用直方图形状判定椒盐噪声的两种灰度值,用于噪声像素的检测与定位。对于非噪声像素,直接输出灰度值;对于噪声像素,沿其邻域的k个方向分别搜索一个距离最近的非噪声像素,然后以欧式距离倒数为权重,采用k个非噪声像素的加权灰度均值作为噪声像素的输出灰度值。测试了不同的方向数k对滤波性能的影响,确定了k的最佳取值为4。采用该方法对椒盐噪声密度为10%到90%的图像进行滤波,输出图像的峰值信噪比比现有同类方法提高了1.8~4.7 d B。该方法有效提高了高密度椒盐噪声图像的滤波质量,处理速度满足实时要求。
