Under-Sampled High-Dimensional Data Recovery via Symbiotic Multi-Prior Tensor Reconstruction
This work addresses the challenge of data recovery in under-sampled scenarios for applications in sensing technology, though it appears incremental as it builds on existing tensor reconstruction methods.
The paper tackled the problem of recovering high-dimensional data from extremely low sampling rates by proposing a tensor reconstruction method that integrates multiple priors, achieving superior performance compared to state-of-the-art methods on datasets like color images, hyperspectral images, and grayscale videos.
The advancement of sensing technology has driven the widespread application of high-dimensional data. However, issues such as missing entries during acquisition and transmission negatively impact the accuracy of subsequent tasks. Tensor reconstruction aims to recover the underlying complete data from under-sampled observed data by exploring prior information in high-dimensional data. However, due to insufficient exploration, reconstruction methods still face challenges when sampling rate is extremely low. This work proposes a tensor reconstruction method integrating multiple priors to comprehensively exploit the inherent structure of the data. Specifically, the method combines learnable tensor decomposition to enforce low-rank constraints of the reconstructed data, a pre-trained convolutional neural network for smoothing and denoising, and block-matching and 3D filtering regularization to enhance the non-local similarity in the reconstructed data. An alternating direction method of the multipliers algorithm is designed to decompose the resulting optimization problem into three subproblems for efficient resolution. Extensive experiments on color images, hyperspectral images, and grayscale videos datasets demonstrate the superiority of our method in extreme cases as compared with state-of-the-art methods.