Abstract
In this paper, we propose a novel video reconstruction methodology which is built based on alternating direction method of multipliers (ADMM) algorithm. In this regard, a new enhanced ADMM model has been used which permits the user to apply image or video reconstruction techniques as sub-problems being embedded to a denoising methodology. Correspondingly, we use conventional compressive sensing (CS) based Gaussian mixture models (GMM) as a subproblem of our proposed framework. On the other hand, sparse 3D transform-domain block matching (BM3D) is used as the denoiser of algorithm in order to remove the remaining artifacts and noise in the reconstructed video frames. Consequently, by considering both online and offline CS-based GMM frameworks, we are able to make two forms of GMM based video reconstruction algorithms which are represented as online and offline structures. Using the proposed algorithms, video reconstruction is more satisfactory in terms of visual quality in comparison with other state of the art techniques.
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