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Proceedings Paper

A convolution-deconvolution method for improved storage and communication of remotely-sensed image data
Author(s): Gabriel Scarmana; Kevin McDougall
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Paper Abstract

An essential feature of remote sensing and digital photogrammetric processes is image compression and communication over digital links. This paper investigates the probability of using a convolution-deconvolution method as a pre-post-processing step in standard digital image compression and restoration. As such, the paper relates to image coding and compression systems whereby an original image can be transmitted or stored in a convolved (i.e. blurred) representation which renders it more compressible. The image is then thoroughly restored to its original state by reversing the convolution process.

The compressibility of an image increases with blurring, whereby the relation between the compression ratio (CR) and the blurring scale is almost linear. Hence, by convolving by way of a localised response function (i.e. a linear kernel) and thereby blurring an image before compression, the CR will increase accordingly. In this novel process the response function is applied to a fractal one-dimensional representation of a given image. A blurred image is thus created, which can be shown to contain the details of the original image and thereby restored by reversing the blurring process. The implications of increased CR are examined in terms of the quality of the reconstructed images.

Paper Details

Date Published: 13 November 2018
PDF: 6 pages
Proc. SPIE 10780, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VII, 107800Z (13 November 2018); doi: 10.1117/12.2324451
Show Author Affiliations
Gabriel Scarmana, Univ. of Southern Queensland (Australia)
Kevin McDougall, Univ. of Southern Queensland (Australia)

Published in SPIE Proceedings Vol. 10780:
Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VII
Allen M. Larar; Makoto Suzuki; Jianyu Wang, Editor(s)

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