基于图像内容自适应矩阵完成的高动态范围成像方法

摘要

高动态范围图像合成中容易出现鬼影现象,而传统的矩阵完成算法没有考虑多曝光图像序列的运动特征,导致高动态范围图像中的鬼影未能完全去除.针对这一问题,本文提出一种基于低动态范围图像内容自适应矩阵完成算法以去除高动态范围图像中的鬼影.首先,根据图像的亮度和色度信息,确定低动态范围图像的运动区域;然后根据这一运动先验信息,调整矩阵完成算法过程中的正则化约束强度,从而得到每幅低动态范围图像的背景信息;最后,考虑到不同曝光度的图像在每一区域所包含的细节不同,采用与曝光相关的融合策略合成去鬼影的高动态范围图像.实验分别采用简单背景图像序列和复杂背景图像序列,结果表明,所提出的算法相比于奇异值部分和最小化矩阵完成算法,能取得更好的合成效果,适用于复杂背景下的高动态范围图像合成.%High dynamic range (HDR) imaging usually produces ghosting artifacts, while the traditional matrix completion (MC) method may fail to completely remove the ghosts, without considering the motion characteristics of multi-exposure image. To solve this problem, this paper presents a new HDR imaging method based on content adaptive matrix completion of low dynamic range (LDR) image to remove the ghosts of HDR image. Firstly, according to the image luminance and chrominance information, the LDR image motion area is determined. Then, based on the priori information of motion,the regularization constraint intensity is adjusted in MC process to get each LDR image background information. Finally,a fusion strategy related to multiple exposures is proposed while the difference of details in each image area under different exposures is considered. Regular background sequences and cluttered background sequences are used for experiments.The experimental results demonstrate that, compared with the partial sum minimization of singular values-matrix completion method, the proposed method is more real-time and suitable for cluttered background sequences.