It is difficult to develop image reconstruction algorithms for tomographic gamma scanning based on drummed radioactive residues or wastes.In this paper,a novel reconstruction algorithm of transmission image for tomogr...It is difficult to develop image reconstruction algorithms for tomographic gamma scanning based on drummed radioactive residues or wastes.In this paper,a novel reconstruction algorithm of transmission image for tomographic gamma scanning is proposed.It is based on the conventional transmission equation and equivalent gamma-ray track length modified by a Monte Carlo method.The algorithm is implemented by simulating the samples on the established platform.For the verification experiments of the algorithm,several cubic voxel samples were designed and manufactured.Experimental tests were conducted.The tomographic gamma scanning of transmission images is compared with the linear attenuation coefficients by the simulated values and experimental data with the algorithm and the reference values.The results show that the absolute relative errors of the reconstructed images are less than 5%.展开更多
针对低光照图像整体对比度低、细节显示不够清晰的问题,提出一种结合自适应伽马(Gamma)变换和带颜色恢复的多尺度Retinex(multi-scale Retinex with color restoration,MSRCR)算法的低光照图像增强方法。首先,为了动态地拉伸图像灰度值...针对低光照图像整体对比度低、细节显示不够清晰的问题,提出一种结合自适应伽马(Gamma)变换和带颜色恢复的多尺度Retinex(multi-scale Retinex with color restoration,MSRCR)算法的低光照图像增强方法。首先,为了动态地拉伸图像灰度值范围和提高图像对比度,进行RGB到HSV的颜色空间转换,采用多尺度融合方法提取图像的光照分量,并结合Gamma校正曲线实现图像自适应Gamma变换,提升图像的对比度;其次,针对自适应Gamma增强后的图像亮度较低的问题,采用MSRCR算法进一步提升图像亮度,并结合小波重构方法融合自适应Gamma变换后的图像和MSRCR增强后的图像;最后,由于小波重构后的图像局部存在过曝、过饱和的缺陷,结合基于模拟退火的自适应融合方法,将自适应Gamma变换后的图像和小波重构后的图像进行融合,得到最终的增强图像。所提方法既提高了低光照图像的对比度,使图像更有质感,又提升了图像的整体亮度,使暗部区域细节更加清晰;同时,弥补了MSRCR算法易出现色偏、颜色失真的缺陷。将所提方法应用于LOL低光照图像数据集,并与经典的图像增强算法进行对比。实验结果表明,所提方法使图像质量平均提高70%,图像结构相似性(structural similarity,SSIM)指数平均提高30%,图像信息熵平均提高20%,不仅提升了图像的对比度和亮度,而且避免了过曝、色偏、颜色失真等问题的出现。展开更多
基金Supported by the Foundation for Returned Oversea Chinese Scholars(No.33)
文摘It is difficult to develop image reconstruction algorithms for tomographic gamma scanning based on drummed radioactive residues or wastes.In this paper,a novel reconstruction algorithm of transmission image for tomographic gamma scanning is proposed.It is based on the conventional transmission equation and equivalent gamma-ray track length modified by a Monte Carlo method.The algorithm is implemented by simulating the samples on the established platform.For the verification experiments of the algorithm,several cubic voxel samples were designed and manufactured.Experimental tests were conducted.The tomographic gamma scanning of transmission images is compared with the linear attenuation coefficients by the simulated values and experimental data with the algorithm and the reference values.The results show that the absolute relative errors of the reconstructed images are less than 5%.
文摘针对低光照图像整体对比度低、细节显示不够清晰的问题,提出一种结合自适应伽马(Gamma)变换和带颜色恢复的多尺度Retinex(multi-scale Retinex with color restoration,MSRCR)算法的低光照图像增强方法。首先,为了动态地拉伸图像灰度值范围和提高图像对比度,进行RGB到HSV的颜色空间转换,采用多尺度融合方法提取图像的光照分量,并结合Gamma校正曲线实现图像自适应Gamma变换,提升图像的对比度;其次,针对自适应Gamma增强后的图像亮度较低的问题,采用MSRCR算法进一步提升图像亮度,并结合小波重构方法融合自适应Gamma变换后的图像和MSRCR增强后的图像;最后,由于小波重构后的图像局部存在过曝、过饱和的缺陷,结合基于模拟退火的自适应融合方法,将自适应Gamma变换后的图像和小波重构后的图像进行融合,得到最终的增强图像。所提方法既提高了低光照图像的对比度,使图像更有质感,又提升了图像的整体亮度,使暗部区域细节更加清晰;同时,弥补了MSRCR算法易出现色偏、颜色失真的缺陷。将所提方法应用于LOL低光照图像数据集,并与经典的图像增强算法进行对比。实验结果表明,所提方法使图像质量平均提高70%,图像结构相似性(structural similarity,SSIM)指数平均提高30%,图像信息熵平均提高20%,不仅提升了图像的对比度和亮度,而且避免了过曝、色偏、颜色失真等问题的出现。
