Study of an image restoration method based on Poisson-maximum likelihood estimation method for earthquake ruin scene

Yanxing Song; Jingsong Yang; Lina Cheng; Shucong Liu

doi:10.1117/12.2067775

2 September 2014 Study of an image restoration method based on Poisson-maximum likelihood estimation method for earthquake ruin scene

Yanxing Song, Jingsong Yang, Lina Cheng, Shucong Liu

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Proceedings Volume 9284, 7th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronics Materials and Devices for Sensing and Imaging; 92840E (2014) https://doi.org/10.1117/12.2067775
Event: 7th International Symposium on Advanced Optical Manufacturing and Testing Technologies (AOMATT 2014), 2014, Harbin, China

Abstract

An image restoration method based on Poisson-maximum likelihood estimation method (PMLE) for earthquake ruin scene is proposed in this paper. The PMLE algorithm is introduced at first, and automatic acceleration method is used in the algorithm to accelerate the iterative process, then an image of earthquake ruin scene is processed with this image restoration method. The spectral correlation method and PSNR (peak signal-to-noise ratio) are chosen respectively to validate the restoration effect of the method, the simulation results show that iterations in this method will effect the PSNR of the processed image and operation time, and this method can restore image of earthquake ruin scene effectively and has a good practicability.

Citation Download Citation

Yanxing Song, Jingsong Yang, Lina Cheng, and Shucong Liu "Study of an image restoration method based on Poisson-maximum likelihood estimation method for earthquake ruin scene", Proc. SPIE 9284, 7th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronics Materials and Devices for Sensing and Imaging, 92840E (2 September 2014); https://doi.org/10.1117/12.2067775

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KEYWORDS

Image processing

Earthquakes

Image restoration

Signal processing

Image quality

Point spread functions

Signal to noise ratio

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