Self-tuning underwater image restoration

Emanuele Trucco; Adriana T. Olmos-Antillon

doi:10.1109/JOE.2004.836395

Self-tuning underwater image restoration

Emanuele Trucco, Adriana T. Olmos-Antillon

School of Engineering & Physical Sciences

Research output: Contribution to journal › Article › peer-review

165 Citations (Scopus)

Abstract

A self-tuning image restoration filter based on a simplified version of the Jaffe-McGlamery underwater image formation model is presented. Optimal values of the filter parameters are estimated automatically for each individual image by optimizing a quality criterion based on a global contrast measure. The simplified model is ideally suitable for diffuse-light imaging with limited backscatter, but qualitative tests show good performance in a variety of imaging conditions. In addition, quantitative tests with a large number of frames from six real mission videos indicate a substantial performance improvement when restoration is used as a preprocessor for a classifier detecting man-made objects in unconstrained subsea videos. © 2006 IEEE.

Original language	English
Pages (from-to)	511-519
Number of pages	9
Journal	IEEE Journal of Oceanic Engineering
Volume	31
Issue number	2
DOIs	https://doi.org/10.1109/JOE.2004.836395
Publication status	Published - Apr 2006

Keywords

Computer vision
Image restoration
Self-tuning systems

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10.1109/JOE.2004.836395

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abstract = "A self-tuning image restoration filter based on a simplified version of the Jaffe-McGlamery underwater image formation model is presented. Optimal values of the filter parameters are estimated automatically for each individual image by optimizing a quality criterion based on a global contrast measure. The simplified model is ideally suitable for diffuse-light imaging with limited backscatter, but qualitative tests show good performance in a variety of imaging conditions. In addition, quantitative tests with a large number of frames from six real mission videos indicate a substantial performance improvement when restoration is used as a preprocessor for a classifier detecting man-made objects in unconstrained subsea videos. {\textcopyright} 2006 IEEE.",

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AB - A self-tuning image restoration filter based on a simplified version of the Jaffe-McGlamery underwater image formation model is presented. Optimal values of the filter parameters are estimated automatically for each individual image by optimizing a quality criterion based on a global contrast measure. The simplified model is ideally suitable for diffuse-light imaging with limited backscatter, but qualitative tests show good performance in a variety of imaging conditions. In addition, quantitative tests with a large number of frames from six real mission videos indicate a substantial performance improvement when restoration is used as a preprocessor for a classifier detecting man-made objects in unconstrained subsea videos. © 2006 IEEE.

KW - Computer vision

KW - Image restoration

KW - Self-tuning systems

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ER -

Self-tuning underwater image restoration

Abstract

Keywords

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