受限于材料和制造工艺, 红外图像中普遍存在着条纹非均匀性, 其严重影响了图像的成像效果, 进而对后续的目标识别、检测等工作造成干扰。典型的最小均方误差(LMS)算法在一定程度上可以抑制条纹非均匀性, 但其场景适应性差, 存在拖尾和“鬼影”现象。提出一种改进型的最小均方误差(LMS)自适应滤波算法对图像进行处理, 利用双边滤波和最速下降法快速获取准确的校正参数, 将前一帧算出的校正结果作为后一帧的初始输入值, 提升算法的准确性, 同时算法还增加了边缘检测模块以保留图像细节。采用不同场景下非制冷型探测器的真实红外图像, 从主观和客观两个方面对比了本算法和经典LMS算法, 结果表明, 提出的算法可以很好地保护图像细节, 也具有良好的场景适应性。

Due to the limitations of materials and manufacturing processes, stripe non-uniformity is commonly present in infrared images, which seriously affects the imaging effect of the image and subsequently interferes with subsequent target recognition, detection, and other work. The classic least mean square error (LMS) algorithm can suppress stripe non-uniformity to a certain extent, but its scene adaptability is poor, and there are trailing and “ghost” phenomena. This article proposes an improved least mean square error (LMS) adaptive filtering algorithm for image processing, which utilizes bilateral filtering and steepest descent method to quickly obtain accurate correction parameters. The correction results calculated from the previous frame are used as the initial input values for the following frame, improving the accuracy of the algorithm. At the same time, the algorithm also adds an edge detection module to preserve image details. The article uses real infrared images of non cooled detectors in different scenarios, and compares the algorithm proposed in this paper with the classic LMS algorithm from both subjective and objective aspects. The results show that the algorithm proposed in this paper can effectively protect image details and has good scene adaptability.