In this paper, entropy of singular values is used to promote the low rank property which accounts the inherent spatial redundancy and spectral correlation. Inspired from the connection between randomness and compactness of signals, we minimise ENtropy for low rank Tensor Completion (ENTC) using tensor-Singular Value Decomposition (t-SVD) framework. This approach assures better performance in excessive loss and complex low rank structure scenarios. As opposed to state-of- the-art-methods, the proposed approach outperforms with a fewer number of reliable samples and extends the region of recovery. The significant performance improvement is observed in terms of Inverse Relative Squared Error (iRSE), Average Structural SIMilarity (ASSIM), and Average Feature SIMilarity (AFSIM) measures.

中文翻译：

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用于视频恢复的基于熵的重加权张量补全技术

在本文中，奇异值的熵用于提升低秩属性，该属性考虑了固有的空间冗余和谱相关性。受信号的随机性和紧凑性之间的联系的启发，我们使用张量-奇异值分解 (t-SVD) 框架最小化低秩张量完成 (ENTC) 的熵。这种方法可确保在过度损失和复杂的低秩结构场景中获得更好的性能。与最先进的方法相反，所提出的方法在可靠样本数量较少的情况下优于其他方法，并扩展了恢复区域。在逆相对平方误差 (iRSE)、平均结构相似度 (ASSIM) 和平均特征相似度 (AFSIM) 度量方面观察到显着的性能改进。