A convolution-deconvolution method for improved storage and communication of remotely-sensed image data

Scarmana, Gabriel and McDougall, Kevin (2018) A convolution-deconvolution method for improved storage and communication of remotely-sensed image data. In: 11th SPIE Asia-Pacific Remote Sensing Symposium 2018: Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VII, 24-26 Sept 2018, Honolulu, Hawaii, United States.

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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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published version deposited in accordance with the copyright policy of the publisher. Copyright 2018 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying
Date Deposited: 19 Nov 2018 07:21
Last Modified: 15 Jan 2019 03:02
Uncontrolled Keywords: image compression, image convolution, image deconvolution, image restoration
Fields of Research : 08 Information and Computing Sciences > 0804 Data Format > 080401 Coding and Information Theory
09 Engineering > 0909 Geomatic Engineering > 090903 Geospatial Information Systems
09 Engineering > 0909 Geomatic Engineering > 090905 Photogrammetry and Remote Sensing
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering
Identification Number or DOI: 10.1117/12.2324451
URI: http://eprints.usq.edu.au/id/eprint/35131

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