Scarmana, Gabriel and McDougall, Kevin 
ORCID: https://orcid.org/0000-0001-6088-1004
(2020)
A process for the accurate reconstruction of pre-filtered and compressed digital aerial images.
In: 11th International Symposium on Digital Earth (ISDE 11), 4-27 Sept 2019, Florence, Italy.
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Abstract
The study of compression and decompression methods is crucial for storage and/or transmission of large numbers of image data which is required for archiving aerial photographs, satellite images and digital ortho-photos. Hence, the proposed work aims to increment the compression ratio (CR) of digital images in general. While emphasis is made on aerial images, the same principle may find applications to other types of raster based images.
The process described here involves the application of pre-defined low-pass filters (i.e. kernels) prior to applying standard image compression encoders. Low-pass filters have the effect of increasing the dependence between neighbouring pixels which can be used to improve the CR. However, for this pre-filtering process to be considered as a compression instrument, it should allow for the original image to be accurately restored from its filtered counterpart.
The development of the restoration process presented in this study is based on the theory of least squares and assumes the knowledge of the filtered image and the low-pass filter applied to the original image. The process is a variant of a super-resolution algorithm previously described, but its application and adaptation to the filtering and restoration of images, in this case (but not exclusively) aerial imagery, using a number of scales and filter dimensions is the expansion detailed here. An example of the proposed process is detailed in the ensuing sections. The example is also indicative of the degree of accuracy that can be attained upon applying this process to gray-scale images of different entropies and coded in a lossy or lossless mode.
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| Item Type: | Conference or Workshop Item (Commonwealth Reporting Category E) (Poster) |
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| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
| Faculty/School / Institute/Centre: | Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 -) |
| Faculty/School / Institute/Centre: | Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 -) |
| Date Deposited: | 22 Jul 2020 01:58 |
| Last Modified: | 13 Oct 2020 04:51 |
| Uncontrolled Keywords: | image reconstruction, image Convolution, image Filtering |
| Fields of Research (2008): | 01 Mathematical Sciences > 0103 Numerical and Computational Mathematics > 010301 Numerical Analysis 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 Objectives (2008): | E Expanding Knowledge > 97 Expanding Knowledge > 970104 Expanding Knowledge in the Earth Sciences |
| Identification Number or DOI: | https://doi.org/10.1088/1755-1315/509/1/012047 |
| URI: | http://eprints.usq.edu.au/id/eprint/39039 |
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