基于大数据分析的可见光图像融合质量评价研究

Research on quality evaluation of visible light image fusion based on big data analysis

在复杂可见光图像下图像融合质量受到遮挡和重叠等因素影响,需要进行图像融合质量评价优化设计,提出基于大数据分析的可见光图像融合质量评价模型,采用相应图像块之间的视觉特征提取方法建立可见光图像的深度立体匹配模型,将不同光照强下采集的图像像素值显示映射到嵌入特征空间中,完成预处理,构建可见光图像的动态像素大数据融合模型,通过端到端的视差融合估计实现对可见光图像的动态融合,采用超分辨重建方法获得真实配对图像,分析SR结果与LR图像中的相似内容,以特征级别的图像分布域反映可见光图像融合质量评价,实现可见光图像融合质量评价。仿真结果表明,采用该方法进行可见图像融合的匹配性能较好,图像的对比度、饱和度高,提高了可见光的成像质量,耗时为0.012 s,平均迭代次数为1.569,并且均方误差仅为1.071,总误差仅为4.646,该方法有效提高了图像融合质量的同时,提高了评估效果。

The quality of image fusion in complex visible light images is affected by factors such as occlusion and overlap.It is necessary to optimize the design of image fusion quality evaluation.A visible light image fusion quality evaluation model based on big data analysis is proposed,and a deep stereo matching model for visible light images is established using the visual feature extraction method between corresponding image blocks,Map the pixel values of images collected under different lighting intensities to the embedded feature space,finish preprocessing,construct a dynamic pixel big data matching model for visible light images,achieve dynamic fusion of visible light images through end-to-end disparity fusion estimation,and the superresolution reconstruction method was used to obtain the real paired images,and the similar contents of SR results and LR images were analyzed.The feature level image distribution domain was used to reflect the visible image fusion quality evaluation,and the visible image fusion quality evaluation was realized.The simulation results show that the matching performance of visible image fusion using this method is better,the image contrast and saturation are high,and the imaging quality of visible light is improved.The time consuming is 0.012 s,the average number of iterations is 1.569,and the mean square error is only 1.071,and the total error is only 4.646.The method effectively improves the image fusion quality.Improve the evaluation effect.