HDR Imaging Based on Low-rank Matrix Completion and Total Variation Constraint
摘要
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.