摘要：

基于低秩矩阵填充(LRMC)的高动态范围(HDR)成像模型通过恢复运动目标遮挡的背景信息,去除运动目标在结果图像中的鬼影。但该模型忽略图像的局部空间信息,不能有效恢复HDR图像的边缘。为解决该问题,将运动目标的空间分段平滑性作为额外约束,提出一种LRMC和全变分约束相结合的背景恢复模型,同时结合低动态范围背景图像的低秩性以及运动目标的稀疏性和分段平滑性给出模型的数值计算方法。实验结果表明,与基于LRMC的HDR成像方法相比,该方法能够提高边缘处理能力,较好地恢复图像的边缘。

关键词: 高动态范围成像, 低秩矩阵填充, 全变分, 背景恢复, 秩最小化

Abstract:

The High Dynamic Range(HDR) imaging method based on Low-rank Matrix Completion(LRMC) removes the ghost image of the moving target in the resulting image by restoring the background information of the moving target occlusion.However,since the model ignores the local spatial information of the image,the edge of the HDR image cannot be effectively restored.In order to solve this problem,the spatial segmentation smoothness of moving targets is taken as an additional constraint.A background restoration model combining LRMC and total variational constraints is proposed,and the numerical calculation method of the model is given.At the same time,the low rank and sparsity and segmentation smoothness of moving targets of low dynamic range background image is utilized.Experimental results show that the proposed method can improve edge processing ability,and recovers edges well compared with two existing LRMC-based HDR imaging methods.

Key words: High Dynamic Range(HDR) imaging, Low-rank Matrix Completion(LRMC), total variation, background recovery, rank minimization

中图分类号: 

• TP391