Achieving complete and accurate traffic data as input is crucial for most intelligent transportation systems. However, due to hardware or software malfunction, inevitably faced with missing noise problems. Most of the existing representation-based recovery methods adopt sparse representation theory, which well models local association properties data, but ignores their global correlation. To ov...

Compressive spectral imaging (CSI) has emerged as an alternative image acquisition technology, which reduces the number of measurements at cost requiring a recovery process. In general, reconstruction methods are based on hand-crafted priors used regularizers in optimization algorithms or recent deep neural networks employed generator to learn non-linear mapping from low-dimensional compressed ...

The enormous amount of data that are generated by hyperspectral remote sensing images (HSI) combined with the spatial channel’s limited and fragile bandwidth creates serious transmission, storage, application challenges. HSI reconstruction based on compressed has become a frontier area, its effectiveness depends heavily exploitation sparse representation information correlation. In this paper, ...