ABSTRACT
Spectral videos contain highly redundant information across spatial, spectral and temporal axes which can be exploited through a temporal-data-learned sparsifying basis. However, in compressive spectral video acquisition, tackling dictionary learning is time-consuming since it increases the computational complexity and presents drawbacks for realtime processing, where offline learning is required. This paper introduces a tensor-decomposition learning (TenDL) framework for simultaneous online sparsifying and recovering the spatialspectral- temporal information of a spectral video performed on several temporal superpixels (TSP-TenDL) for time processing reduction. The framework is composed of two main stages: preprocessing and joint estimation. The preprocessing stage includes a strategy for a grayscale approximation of the video to provide a suitable initialization of the sparsifying basis to be learned. To fully exploit the high signal correlation, a set of temporal superpixels is estimated from the grayscale approximation, reducing the reconstruction time of the large-scale data. Then, the outcome of the first stage is used to estimate the basis and the signal coefficients, where an optimization problem is solved to learn and reconstruct the basis and the signal, respectively, following a block-descent coordinate strategy. The proposed approach is compared from simulations with an offline-learned based method, traditional matrix-based recovery algorithms and the tensor-based recovery, the two latter using a fixed basis, where TSP-TenDL exhibits higher image quality results and lower computation time. Specifically, our methodology gains up to 7dB in terms of PSNR and a speedup of up to 6.6× compared with state-of-the-art counterparts.
ABSTRACT
Compressive spectral video sensing (CSVS) systems obtain spatial, spectral, and temporal information of a dynamic scene through the encoding of the incoming light rays by using a temporal-static coded aperture (CA). CSVS systems use CAs with binary entries spatially distributed at random. The random spatial encoding of the binary CAs entails a poor quality in the reconstructed images even though the CSVS sensing matrix is incoherent with the sparse representation basis. In addition, since some pixels are totally blocked, information such as object motion is missed over time. This paper substitutes the temporal-static binary coded apertures by a richer spatio-spectro-temporal encoding based on selectable color filters, named temporal colored coded apertures (T-CCA). The spatial, spectral, and time distributions of the T-CCAs are optimized by better satisfying the restricted isometry property (RIP) of the CSVS system. The RIP-optimized T-CCAs lead to spatio-spectral-time structures that tend to sense more uniformly the spatial, spectral, and temporal dimensions. An algorithm for optimally designing the T-CCAs is developed. In addition, a regularization term based on the scene motion is included in the inverse problem leading to a better quality of the reconstructed images. Computational experiments using four different spectral videos show an improvement of up to 6 dB in terms of peak signal-to-noise ratio of the reconstructed images by using the proposed inverse problem and the T-CCA patterns compared with the binary CAs and random and image-optimized CCA patterns.
