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Proceedings Paper

Image enhancement of range gated underwater imaging system based on least square error
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Paper Abstract

Range gated underwater laser imaging technique can eliminate backscattering noise effectively. While the images associated with underwater imaging systems are normally degraded seriously by the intervening water medium. And the speckle noise is especially severe for the reason that we adopt the system based on intensified gate imaging technology. Well known causes of image degradation underwater include turbidity, particulate matters in the water column, and the interaction between light and medium as light travels through water. Consequently, using full image formation models to design restoration algorithms is more complex in water than in air because it's hard to get the values of the model parameters relating to water properties, e.g., attenuation and scattering coefficients. To improve the quality of the low signal-to-noise ratio images obtained through range gated laser imaging system, an enhancement algorithm is proposed. The main purpose of the algorithm proposed for processing underwater images is to filter out unwanted noises and remain desired signals. This algorithm is based on the principle of the least square error method, which fits discrete image data to continuous piecewise curves. To simply the fitting of image data, the interval of each row and column is subdivided into several subintervals. Then a curve is used to fit the image data within the subinterval. To merge two adjacent lines together, a weighting technique with a linear weighting factor is imposed. A series of experiments are carried out to study the effects of the algorithm. And the signal-to-noise ratio shows that the proposed algorithm can achieve high quality enhancement images.

Paper Details

Date Published: 2 September 2009
PDF: 8 pages
Proc. SPIE 7443, Applications of Digital Image Processing XXXII, 74432B (2 September 2009); doi: 10.1117/12.832187
Show Author Affiliations
Hailan Li, Beijing Institute of Technology (China)
Xia Wang, Beijing Institute of Technology (China)
Tingzhu Bai, Beijing Institute of Technology (China)
Weiqi Jin, Beijing Institute of Technology (China)
Xiao Zhang, Beijing Institute of Technology (China)
Lin Zhao, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 7443:
Applications of Digital Image Processing XXXII
Andrew G. Tescher, Editor(s)

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