基于纹理显著性的微光图像目标检测

微光图像对比度较低,目标显著性不明显,目标自动探测难度大.针对此问题,本文提出一种噪声鲁棒性较好的图像局部纹理粗糙度算法,并给出一种适用于微光图像显著分析的纹理显著性算法.首先,提出一种新的局部纹理粗糙度算法,该算法利用最佳尺寸计算局部纹理粗糙度,对纹理图像进行加噪实验,与基于局部分形维的粗糙度方法相比,本文局部纹理粗糙度算法表现出较好的噪声鲁棒性;其次,在提取图像粗糙度特征图的基础上,给出一种针对纹理的显著性度量算法;最后,将纹理显著性算法应用于微光图像目标检测,实验结果证明了该算法的有效性.

Low light level image target detection based on texture saliency

Owing to its low contrast, the target of low light level (LLL) image is not very salient, and it is difficult to detect automatically. Aimed at this problem, this paper proposes a noise robustness algorithm for computing the local texture coarseness (LTC) of textured images, and provides a texture saliency (TS) calculation method that is applicable to saliency analysis of LLL image. Firstly, we present a novel LTC algorithm, by which the LTC around a pixel using the best size of the pixel. Compared with coarseness measure based on local fractal dimension, the LTC algorithm shows much better noise robustness in the experiments of noised textured images. Then, a TS algorithm is given based on the extraction of texture coarseness feature map. Finally, we apply the TS algorithm to LLL image target detection, which is efficient proved by experimental results.
Keywords：local texture coarsenesstexture saliencysaliency calculationLLL image target detection