SLIC super-pixels for multi-resolution compressive spectral imaging reconstruction
SLIC super-pixels for multi-resolution compressive spectral imaging reconstruction is a scholarly work, published in 2018 in ''Optica Pura y Aplicada''. The main subjects of the publication include noise reduction, artificial intelligence, computer vision, remote sensing, superpixel, photoacoustic imaging, image resolution, geology, compressed sensing, pixel, computer science, and resolution. Spectral imaging (SI) is widely used in different applications involving material identification since it contains both spatial (, ) and spectral information ().However, traditional SI scanning methods involve massive amounts of data, which increase the cost of storing and processing.Compressive sensing (CS) theory has been applied in SI, such that the underlying data cube can be recovered from a reduced number of measures.Reconstructions are obtained by 2 - 1 norm-based algorithms whose computational complexity grows in proportion to the number of unknowns.In this paper, a multiresolution reconstruction model based on the simple linear iterative clustering (SLIC) is proposed to reduce the number of unknown values to recover.Simulation results show that the proposed method is up to 86% faster than the full-resolution reconstructions.Additionally, MR approach obtains more accurate reconstructions with improvements of up to 12dB of PSNR.