Recently, a two-dimensional （2-D） Tsallis entropy thresholding method has been proposed as a new method for image segmentation. But the computation complexity of 2-D Tsallis entropy is very large and becomes an obst...Recently, a two-dimensional （2-D） Tsallis entropy thresholding method has been proposed as a new method for image segmentation. But the computation complexity of 2-D Tsallis entropy is very large and becomes an obstacle to real time image processing systems. A fast recursive algorithm for 2-D Tsallis entropy thresholding is proposed. The key variables involved in calculating 2-D Tsallis entropy are written in recursive form. Thus, many repeating calculations are avoided and the computation complexity reduces to O（L2） from O（L4）. The effectiveness of the proposed algorithm is illustrated by experimental results.展开更多
红外和可见光图像因其互补性而广泛应用于多个领域。但是,由于红外目标提取的不足,导致直接合成融合图像会存在失真以及信息丢失等问题。本文提出了一种基于快速滚动引导滤波(fast rolling guidance filter, FRGF)和改进的遗传算法的红...红外和可见光图像因其互补性而广泛应用于多个领域。但是,由于红外目标提取的不足,导致直接合成融合图像会存在失真以及信息丢失等问题。本文提出了一种基于快速滚动引导滤波(fast rolling guidance filter, FRGF)和改进的遗传算法的红外与可见光图像融合算法。首先,将对输入的红外图像和可见光图像进行FRGF多尺度分解,得到基底层和细节层图像。然后,基于改进的遗传算法和Renyi熵计算出最优阈值,将红外图像中的目标区域进行提取。最后,基底层使用比较匹配最大熵融合机制进行融合的方法;采用修正的拉普拉斯能量融合细节层。该算法融合了多尺度分解和自适应阈值分割的优点。实验结果表明,本文算法在主客观评价指标方面均优于多种经典融合算法,能够生成良好的融合结果。展开更多
基金supported by the National Natural Science Foundation of China for Distinguished Young Scholars(60525303)Doctoral Foundation of Yanshan University(B243).
文摘Recently, a two-dimensional （2-D） Tsallis entropy thresholding method has been proposed as a new method for image segmentation. But the computation complexity of 2-D Tsallis entropy is very large and becomes an obstacle to real time image processing systems. A fast recursive algorithm for 2-D Tsallis entropy thresholding is proposed. The key variables involved in calculating 2-D Tsallis entropy are written in recursive form. Thus, many repeating calculations are avoided and the computation complexity reduces to O（L2） from O（L4）. The effectiveness of the proposed algorithm is illustrated by experimental results.
文摘红外和可见光图像因其互补性而广泛应用于多个领域。但是,由于红外目标提取的不足,导致直接合成融合图像会存在失真以及信息丢失等问题。本文提出了一种基于快速滚动引导滤波(fast rolling guidance filter, FRGF)和改进的遗传算法的红外与可见光图像融合算法。首先,将对输入的红外图像和可见光图像进行FRGF多尺度分解,得到基底层和细节层图像。然后,基于改进的遗传算法和Renyi熵计算出最优阈值,将红外图像中的目标区域进行提取。最后,基底层使用比较匹配最大熵融合机制进行融合的方法;采用修正的拉普拉斯能量融合细节层。该算法融合了多尺度分解和自适应阈值分割的优点。实验结果表明,本文算法在主客观评价指标方面均优于多种经典融合算法,能够生成良好的融合结果。
