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Tecnura

versão impressa ISSN 0123-921X

Resumo

MARIN ALFONSO, Jeison; BETANCUR AGUDELO, Leonardo  e  ARGUELLO FUENTES, Henry. Further compression of focal plane array in compressive spectral imaging architectures. Tecnura [online]. 2017, vol.21, n.52, pp.45-52. ISSN 0123-921X.  http://dx.doi.org/10.14483/udistrital.jour.tecnura.2017.2.a03.

Abstract Context: It is possible to capture High-resolution 3D hyper-spectral images in a single 2D image through techniques based on compressed sensing. A variety of architectures have proposed Compressive Spectral Imaging (CSI) technique during the last years. An optical camera designed to capture spatio-spectral information of the scene prints projections towards a Focal Plane Array (FPA) giving the capability of storing or transmitting them. Afterwards, the original image can be reconstructed via an 11 -norm-based optimization algorithm. The size in bytes of the FPA measurement is less than the original image; for that reason, this FPA is considered a 2D compressed version of the original 3D image. Objective: To perform a further compression of the FPA measurement for four CSI architectures, in order to increase transfer rates or to decrease storing sizes. Method: In this work, the design of the further compression using arithmetic coding is presented for four CSI architectures, and an inverse transformation is proposed. This transformation is applied to the FPA based on the structure of the optical filters and the coded apertures of the cameras used in the CSI, allowing an increasing in the compression rate. Results: Results show that the compression rate rises between 1 and 2 points in three of the architectures. Conclusions: Despite data loss in the process of transformation-quantification-compression-decompression of the FPA, the quality of the reconstructed data cube (expressed in terms of the PSNR between the reconstructed image and the original one) remains close to the original version with no further compression.

Palavras-chave : Compression Techniques; Compressive Sensing; Compressive Spectral Imaging; Focal Plane Array; Hyperspectral Images.

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