红外图像多尺度统计和应用先验去模糊模型 何易德.pdf

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激光技术
范数
中图分类号：TP391 文献标识码：A


Deblurring model of Infrared image multi-scale statistics
and application of prior
HE Yide, ZHU Bin, JIANG Huhai, LIU Shuxin, LI Liming, HU Shaoyun
（Southwest Institute of Technical Physics, Chengdu 610046, China）

Abstract: In order to improve specific application imaging quality of infrared seeker, statistical image analysis is used to
model about application scene and imaging conditional of infrared image seeker. On the one hand, L1/L2 norm is used to
constrain the restored image according to the characteristics of multi-scale imaging, which keeps details in the iterative
restoration. On the other hand, a sparse Laplacian distribution is used to constrain fuzzy kernel, to maintain image’s content.
Image kernel size can be adjusted adaptively by calculating the image details. The result shows that the prior constrain
algorithm of this paper can effectively improve the image quality. In addition, the evaluation index is improved by this prior
design, the contrast enhancement coefficient index is increased by 20%～50%, the peak signal to noise ratio is increased by
～, and the Cumulative Probability of Blur Detection is increased by ～ study is helpful for complex scene
and moving vector imaging.
Key words: image processing; statistical prior constrain; multi-scale imaging; imaging application scenarios; Laplacian
distribution; L1/L2norm