A method for reversing the effect of digital image blurring: a space domain investigation

Paper


Scarmana, G.. 2023. "A method for reversing the effect of digital image blurring: a space domain investigation." 12th International Symposium on Mobile Mapping Technology (MMT 2023). Padua, Italy 24 - 26 May 2023 Italy. https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-431-2023
Paper/Presentation Title

A method for reversing the effect of digital image blurring: a space domain investigation

Presentation TypePaper
AuthorsScarmana, G.
Journal or Proceedings TitleInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Journal CitationVolume XLVIII-1/W1-2023, pp. 431-435
Number of Pages5
Year2023
Place of PublicationItaly
ISSN1682-1750
2194-9034
Digital Object Identifier (DOI)https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-431-2023
Web Address (URL) of Paperhttps://isprs-archives.copernicus.org/articles/XLVIII-1-W1-2023/431/2023/
Web Address (URL) of Conference Proceedingshttps://isprs-archives.copernicus.org/articles/XLVIII-1-W1-2023/
Conference/Event12th International Symposium on Mobile Mapping Technology (MMT 2023)
Event Details
12th International Symposium on Mobile Mapping Technology (MMT 2023)
Delivery
In person
Event Date
24 to end of 26 May 2023
Event Location
Padua, Italy
Event Web Address (URL)
Abstract

Mobile mapping technology has transformed the way in which we capture and map our surroundings. The widespread use of mobile devices such as smart phones and drones has made data collection more efficient and accessible than ever before. However, the image quality of this data is often compromised due to the static or motion blurs resulting from the device being stationary or moving during the data collection process. This can lead to a loss of information, making the data less useful.
To address this issue, an inverse filtering or deblurring method based on the theory of least squares is examined. This method implements a deconvolution process in the space domain and can restore the original image with a high degree of accuracy if the model of the function or filter that blurred the image is known or can be established.
The accuracy and validity of the deblurring results are presented in terms of the root mean square error (RMSE) of the differences of pixel intensity values between the original and de-blurred images. In tests using grey-scale aerial images of varying entropies and different types of blurring, the RMSE value never exceeded +/- 5 pixel intensity values. This discrepancy is due to the rounding of pixel values resulting from image operations.
The deblurring method presented in this work is an adaptation and extension of a previously described process, tailored specifically for filtering and restoring images - particularly aerial imagery - affected by static and motion blur. This process could also be applied in image compression processes and techniques of transmission over digital links, where blurring filters can suppress noise and increase the dependence between neighbouring pixel values, thereby improving the compression ratio (CR).

KeywordsImage restoration, image blurring, image compression
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460511. Stream and sensor data
Byline AffiliationsSchool of Surveying and Built Environment
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