为解决红外图像弱小目标检测技术中的云层和地物等复杂自然背景抑制这一难题，提出了一种基于多尺度核归一化策略的弱小目标复杂背景抑制新方法。首先，采用波原子变换对图像进行尺度和方向分解，提取图像的多尺度和方向细节特征；然后，根据目标和背景杂波信号子带系数之间的差异，对波原子变换后各子带系数采用设计的核局部归一化调整函数进行修改，以达到有效地抑制原始图像中背景边缘、轮廓和纹理等信息和增强目标信号系数强度的目的；最后，重构调整后各个子带系数获得抑制背景后的目标图像。实验结果显示，与小波和最大中值滤波方法相比较，该方法对包含弱小目标的红外复杂背景都具有良好抑制效果，信杂比增益和背景抑制因子分别提高到3倍和4倍以上。

Complex natural background(e.g. clouds and ground)suppression is a difficult problem for dim and small target detection in infrared image sequences. A dim and small target background suppression method based on wave atoms as a variant of two-dimensional (2D) wavelet packets, is proposed to solve the problem. It adopts kernel local normalization after wave atoms analysis, to suppress background details which contain edge, contour and texture, and enhance target information, and then modified coefficients are reconstructed using wave atoms inverse transform for suppression background. Experimental results demonstrate that, compared with wavelet transform (WT) and max-median (MMed) filter methods, the proposed method can suppress complex background in dim and small target images effectively. The improvement in signal-to-clutter ratio (ISCR) and background suppression factor (BSF) increase more than 3 times and 4 times, respectively.