Block-based reconstructions for compressive spectral imaging

Coded Aperture Snapshot Spectral Imaging system (CASSI) captures spectral information of a scene using a reduced amount of focal plane array (FPA) projections. These projections are highly structured and localized such that each measurement contains information of a small portion of the data cube. Compressed sensing reconstruction algorithms are then used to recover the underlying 3-dimensional (3D) scene. The computational burden to recover a hyperspectral scene in CASSI is overwhelming for some applications such that reconstructions can take hours in desktop architectures. This paper presents a new method to reconstruct a hyperspectral signal from its compressive measurements using several overlapped block reconstructions. This approach exploits the structure of the CASSI sensing matrix to separately reconstruct overlapped regions of the 3D scene. The resultant reconstructions are then assembled to obtain the full recovered data cube. Typically, block-processing causes undesired artifacts in the recovered signal. Vertical and horizontal overlaps between adjacent blocks are then used to avoid these artifacts and increase the quality of reconstructed images. The reconstruction time and the quality of the reconstructed images are calculated as a function of the block-size and the amount of overlapped regions. Simulations show that the quality of the reconstructions is increased up to 6 dB and the reconstruction time is reduced up to 4 times when using block-based reconstruction instead of full data cube recovery at once. The proposed method is suitable for multi-processor architectures in which each core recovers one block at a time.