Video reconstruction based on Intrinsic Tensor Sparsity model

Abstract

The natural images have self-similarities which can be used to improve the image reconstruction. However, the existing video reconstruction algorithms pay more attention to modeling and ignore the importance of priors in the reconstruction. In this paper, the self-similarities are involved in the modeling when the video is reconstructed from temporally compressed video measurements. The proposed reconstruction model includes two parts: First, the video tensor sparsity model is formulated by using a spatial–temporal tensor sparse penalty for similar patches. The Intrinsic Tensor Sparsity (ITS) measure is used as the sparsity measure, which encodes both sparsity insights delivered by the Tucker and CANDECOMP/PARAFAC (CP) decomposition for tensors. Second, 3D video patches are modeled as the Gaussian Joint Sparsity (GJS) by exploiting the temporal similarity to obtain an initial image which has distinct direction structure. GJS is a combination of statistical distribution and joint sparsity model. The experimental results show that both the reconstruction models based on ITS and GJS contribute to improving the quality of the video reconstruction.