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具有高检测率、低虚警率和高检测速度的单帧红外弱小目标检测是一项艰巨的任务,因为目标通常很小且暗淡,并且存在不同类型的干扰,例如高亮背景,复杂的背景边缘和高亮度像素级的噪声点(PNHB).基于HVS的单帧检测算法通常可以实现比传统算法更好的性能,但是,对于基于HVS的算法,如何定义局部对比度的公式是关键问题之一,直接决定算法的性能.到目前为止,研究人员尚未就如何定义局部对比度达成共识,并且已经提出了许多局部对比度定义.现有算法如比值型和差值型的局部对比度算法,不能有效增强真实目标的同时抑制所有干扰,仅以周围区域为背景,而没有考虑周围背景本身的多样性,这些算法浪费了可用于进一步抑制复杂背景的局部多样性信息.提出了一种多尺度比差联合局部对比度检测算法(MRDLCM).它可以结合比值型和差值型算法的优点,因此可以抑制所有类型干扰的同时增强不同大小的真实目标,且不需要任何预处理.此外,提出了基于反向局部多样性(RLD)的权重函数,该函数利用局部周围区域的局部多样性进一步抑制复杂背景.实验结果表明,所提出的MRDLCM_RLD算法相对于现有算法在检测率和误报率上具有有效性和鲁棒性.此外,该算法具有并行处理能力,对于提高检测速度非常有效.“,”Single frame infrared (IR) small dim target detection with high detection rate,low false alarm rate and high detection speed is a difficult task,since the targets are usually very small and dim,and different types of interferences exist,such as high brightness backgrounds,complex background edges and Pixel-sized Noises with High Brightness (PNHB).The single frame detecting algorithms based on HVS can usually achieve a better performance than traditional algorithms.However,for an algorithm based on HVS,how to define the formula for local contrast is one of the key issues,which directly determines the performance of the algorithm.By now,researchers have not reached a consensus on how to define the local contrast,and many local contrast definitions have been proposed.Existing algorithms,such as the ratio form local contrast methods and the difference form local contrast methods,cannot effectively enhance real targets and suppress all the interferences simultaneously,they just simply take the local surrounding areas as background without taking into account the diversity of the local surrounding background itself and the local diversity information which can be used to further suppress the complex backgrounds is wasted.A Multi-scale Ratio-Difference joint Local Contrast Measure (MRDLCM) was proposed.It could combine the advantages of the ratio form methods and the difference form methods,so it could suppress all the types of interferences while enhancing different sizes of real targets,and did not need any preprocessing algorithms.Besides,a weighted function utilizing the Reversed Local Diversity (RLD) was proposed,it utilized the local diversity of the local surrounding areas to suppress the complex backgrounds further.Experimental results show the effectiveness and the robustness of the proposed MRDLCM_RLD algorithm against existing algorithms in detection rate and false alarm rate.Besides,the proposed algorithm has the potential of parallel processing,which is very useful for improving the detection speed.