Motivated by the projects constrained by space capacity and resource transporting time, a project scheduling probIem with capacity constraint was modeled. A hybrid algorithm is proposed, which uses the ideas of bi-lev...Motivated by the projects constrained by space capacity and resource transporting time, a project scheduling probIem with capacity constraint was modeled. A hybrid algorithm is proposed, which uses the ideas of bi-level scheduling and project decomposition technology, and the genetic algorithm and tabu search is combined. Topological reordering technology is used to improve the efficiency of evaluation. Simulation results show the proposed algorithm can obtain satisfied scheduling results in acceptable time.展开更多
针对夜间场景下低照度图像整体亮度不足、边缘难以辨识与色彩失真等问题,在HSV色彩空间的基础上,提出一种基于多尺度自引导锐化-平滑图像滤波(Sharpening-Smoothing Image Fil⁃ter,SSIF)的低照度图像增强方法.首先,利用HSV空间色彩亮度...针对夜间场景下低照度图像整体亮度不足、边缘难以辨识与色彩失真等问题,在HSV色彩空间的基础上,提出一种基于多尺度自引导锐化-平滑图像滤波(Sharpening-Smoothing Image Fil⁃ter,SSIF)的低照度图像增强方法.首先,利用HSV空间色彩亮度分离的特性,对V分量使用多尺度自引导锐化-平滑图像滤波,准确估计光照分量进而求得精确的反射分量.其次,针对光照分量分布不均的问题,提出一种二维自适应伽马变换算法并通过大量对比选取最佳参数,对较暗区域亮度进行拉伸,同时抑制较亮区域的亮度,使整体图像光照更加均匀,图像亮度更符合人眼视觉.再次,针对反射分量存在部分边缘模糊与噪声的问题,提出多尺度钝化掩蔽算法,在抑制噪声的同时能够有效增强图像细节信息,提升整体图像动态范围.最后,对S分量使用自适应饱和度增强算法,将增强后的S分量、V分量与保持不变的H分量合并转到RGB图像,并与带色彩恢复的多尺度视网膜增强算法(Multi-Scale Retinex with Color Restoration,MSRCR)中的色彩恢复因子结合得到最终增强图像.实验结果表明:所提低照度图像增强算法的基于精细自然场景统计的图像质量盲评价指标和平均梯度较其他对比算法分别提高了14.62%、32.10%,不仅能够有效地解决图像亮度分布不均问题,而且能够提高图像轮廓细节的丰富程度和对比度,整体效果优于其他对比算法.展开更多
基金the National Basic Research Program (973 Program) (2002CB312200)
文摘Motivated by the projects constrained by space capacity and resource transporting time, a project scheduling probIem with capacity constraint was modeled. A hybrid algorithm is proposed, which uses the ideas of bi-level scheduling and project decomposition technology, and the genetic algorithm and tabu search is combined. Topological reordering technology is used to improve the efficiency of evaluation. Simulation results show the proposed algorithm can obtain satisfied scheduling results in acceptable time.
文摘针对夜间场景下低照度图像整体亮度不足、边缘难以辨识与色彩失真等问题,在HSV色彩空间的基础上,提出一种基于多尺度自引导锐化-平滑图像滤波(Sharpening-Smoothing Image Fil⁃ter,SSIF)的低照度图像增强方法.首先,利用HSV空间色彩亮度分离的特性,对V分量使用多尺度自引导锐化-平滑图像滤波,准确估计光照分量进而求得精确的反射分量.其次,针对光照分量分布不均的问题,提出一种二维自适应伽马变换算法并通过大量对比选取最佳参数,对较暗区域亮度进行拉伸,同时抑制较亮区域的亮度,使整体图像光照更加均匀,图像亮度更符合人眼视觉.再次,针对反射分量存在部分边缘模糊与噪声的问题,提出多尺度钝化掩蔽算法,在抑制噪声的同时能够有效增强图像细节信息,提升整体图像动态范围.最后,对S分量使用自适应饱和度增强算法,将增强后的S分量、V分量与保持不变的H分量合并转到RGB图像,并与带色彩恢复的多尺度视网膜增强算法(Multi-Scale Retinex with Color Restoration,MSRCR)中的色彩恢复因子结合得到最终增强图像.实验结果表明:所提低照度图像增强算法的基于精细自然场景统计的图像质量盲评价指标和平均梯度较其他对比算法分别提高了14.62%、32.10%,不仅能够有效地解决图像亮度分布不均问题,而且能够提高图像轮廓细节的丰富程度和对比度,整体效果优于其他对比算法.
