Comparison of Different Measurement Matrice for Coded Aperture Snapshot Spectral Imager Multispectral spectral imaging can supply both spatial information and spectral information so it has potential in machine vision. Coded aperture snapshot spectral imager (CASSI) is a fast and compressed multispectral imaging method. The measurement matrice is an indispensable part for signal compression in CASSI, so it greatly influences the quality of the reconstructed signal. In this paper, we mainly analyze and compare several commonly used measurement matrice. The twostep iterative shrinkagethresholding (TwIST) method is used to reconstruct the multispectral data cube from one encoded image and the peak signal to noise ratio (PSNR) is used to judge the reconstruction quality. The random 01 measurement matrice shows the best quality by comparison.

LowComplexity Adaptive Switched PredictionBased Lossless Compression of TimeLapse Hyperspectral Image Data Timelapse hyperspectral image (HSI) data has an enormous size and demands lossless compression for most of the high fidelity applications. In literature, spatial and spectral correlations in HSI are widely studied and used for compression. We propose a novel adaptive switched predictionbased scheme, which efficiently exploits temporal correlations in addition to spatial, and spectral correlations. The predictor switching uses a threshold, which is chosen based on the residual error distribution of already encoded band. Hence, our method does not need any overhead to be transmitted. The proposed scheme outperforms other stateoftheart methods in bitrate, and the method is computationally efficient too.